Coronavirus on Stats and R

EpiLPS for estimation of incubation times

Thu, 01 Aug 2024 00:00:00 +0000

Motivation

A group of researchers from the Data Science Institute (DSI) at Hasselt University developed a new statistical model to estimate the incubation period of a pathogenic organism based on coarse data. The incubation period of an infectious disease (defined as the time elapsed between infection and the manifestation of first symptoms) is of great importance as it permits to shed light on the epidemic potential of a disease and to optimize the length of quarantine periods to freeze transmission. The article (Gressani et al. 2024) was recently published in the American Journal of Epidemiology with practical implementation of the methodology accessible through the EpiLPS package (Gressani et al. 2022).

Coarse data

What makes estimation of incubation times so challenging in the first place? The devil lies in the data. True infection times are stealthy and rarely observed. In information-theoretic jargon this phenomenon is called “imperfect information’’ but statisticians prefer to call it censoring. To be more precise, infection times are interval censored, i.e. one part of the collected data contains exposure intervals $\mathcal{E}=[t^{E_L},t^{E_R}]$ reported by individuals that are part of the study, where $t^{E_L}$ and $t^{E_R}$ stand for the left and right bound, respectively, of the exposure window. The other part of the data contains symptom onset times $t^{\mathcal{S}}$. This is a more easily accessible piece of information -people tend to remember the day when first symptoms appeared- and so the timing of symptom onset is assumed to be exactly observed. Subtracting the exposure bounds from the symptom onset time, one obtains the incubation interval $\mathcal{I}=[t^{\mathcal{I}_L}, t^{\mathcal{I}_R}]$ with lower bound $t^{\mathcal{I}_L}=t^{\mathcal{S}}-t^{E_R}$ and upper bound $t^{\mathcal{I}_R}=t^{\mathcal{S}}-t^{E_L}$, characterizing the coarse data structure which will be the main model input.

Simulated example

In EpiLPS, the estimIncub() routine is designed to compute an estimate of the incubation density based on the methodology of Gressani et al. (2024). Giving a detailed account of the methodology would be out of scope for this blog and the reader is redirected to the article for technicalities. In a nutshell, it is a Bayesian approach making use of (penalized) B-splines, Laplace approximations and Markov chain Monte Carlo (MCMC) methods to derive a semi-parametric estimate of the incubation density. An attractive feature of the estimIncub() routine for the end-user is the minimalistic input it requires to work, namely:

x: A data frame containing the lower and upper bound of the incubation interval.
K: An integer specifying the number of B-splines to smooth the incubation density.
niter: The number of MCMC samples required.

In practice, only the data frame x is required as the remaining inputs are assigned default values. Computationally, the routine requires a small amount of resources as costly subroutines are coded in C++ and integrated in R via the Rcpp package. The structure of x is quite simple. It should be a data frame with two columns containing the left bound $t^{\mathcal{I}_L}$ of the incubation interval (in the first column) and the right bound $t^{\mathcal{I}_R}$.

Let’s start with a simple example where x is simulated based on a data generating mechanism assuming a known incubation distribution. This can be achieved with the incubsim() routine in EpiLPS. We choose x to be generated according to a Lognormal incubation distribution with a mean of 5.5 days and a standard deviation of 2.1 days following Ferretti et al. (2020). Simulation of $n=40$ observations with an average exposure window of 2 days is implemented as follows:

set.seed(2024)
simdat <- incubsim(incubdist = "LogNormal", n = 40, coarseness = 2)
gt(head(simdat$Dobsincub, 5))

tL	tR
3.724500	5.510304
4.381377	6.654224
4.588020	5.483614
3.847835	6.003948
3.522282	5.033487

By typing simdat$Dobsincub, the user has access to the generated incubation intervals (expressed in days), corresponding here to a data frame with two columns and $n=40$ rows. This is the data frame that is injected in the estimIncub() routine:

fit <- estimIncub(x = simdat$Dobsincub, verbose = TRUE)

## ----------------------------------------------------------------
## Time elapsed: 1.339 seconds.
## Fitted density is Log-Normal with meanlog=1.617 and sdlog=0.317.
## Mean incubation period (days): 5.298 with 95% CI: 5.086-5.615.
## 95th percentile (days): 8.484 with 95% CI: 8.121-9.089.
## ----------------------------------------------------------------

The output in the R console can easily be interpreted. It tells us that the model chooses a Lognormal density fit for the incubation period with a mean of 5.3 days (95% CI: 5.0-5.6 days). More detailed summary statistics are accessible by typing fit$stats, such as the posterior standard deviation and additional percentiles. What happens under the hood? Basically, the model computes a semi-parametric fit to the data and compares it with classic parametric fits (Lognormal, Weibull and Gamma) used for incubation modeling. The candidate with the lowest Bayesian information criterion (BIC) wins the game and is finally selected (here the Lognormal distribution). The incubation windows and the fitted incubation density can be obtained by typing:

grid.arrange(plot(fit, typ = "incubwin"), plot(fit, type = "pdf"), nrow = 1)

Real data example

The flexible Bayesian methodology is illustrated on SARS-CoV-2 symptom onset and exposure window data extracted from cases in Vietnam. The dataset was analyzed in Bui et al. (2020) and is publicly available on the GitHub repository provided in the article (https://github.com/longbui/Covid19IncubVN; last accessed July 17, 2024). The dataset contains information about $n=19$ cases identified from January 23, 2020 to April 13, 2020. After continuity corrections (required to change calendar dates into continuous time points), the left and right incubation bounds are given by:

# Left incubation bound
tL <- c(0.504, 2.983, 5.343, 6.969, 6.990, 2.570, 6.870, 1.263, 0.693, 1.869, 1.151, 2.748, 1.209, 1.161, 4.982, 4.017, 2.170, 9.805, 1.659)

# Right incubation bound
tR <- c(1.491, 7.762, 11.777, 12.821, 11.359, 8.551, 24.954, 4.144, 6.408, 7.083, 4.191, 10.326, 9.942, 7.254, 12.690, 10.825, 14.478, 16.592, 18.649)

# Data set
dataVietnam <- data.frame(tL = tL, tR = tR)

The estimIncub() routine provides a Weibull fit with a mean incubation period of 6.7 days (95% CI: 5.8-7.4 days) and the standard deviation (extracted by typing incubfit$stats) is 3.3 days (95% CI: 3.0-3.8 days). A figure of the incubation windows and the fitted Weibull density (with 95% credible interval) is also provided.

set.seed(2024)
incubfit <- EpiLPS::estimIncub(x = dataVietnam, verbose = TRUE, tmax = 25)

## ----------------------------------------------------------------
## Time elapsed: 1.13 seconds.
## Fitted density is Weibull with shape=2.142 and scale=7.521.
## Mean incubation period (days): 6.661 with 95% CI: 5.753-7.375.
## 95th percentile (days): 12.552 with 95% CI: 11.931-14.087.
## ----------------------------------------------------------------

grid.arrange(plot(incubfit, typ = "incubwin"), plot(incubfit, type = "pdf"), nrow = 1)

References

Gressani, O., Torneri, A., Hens, N. and Faes, C. (2024). Flexible Bayesian estimation of incubation times. American Journal of Epidemiology (Accepted manuscript). https://doi.org/10.1093/aje/kwae192

Gressani, O., Wallinga, J., Althaus, C. L., Hens, N. and Faes, C. (2022). EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. PLoS Comput Biol 18(10): e1010618. https://doi.org/10.1371/journal.pcbi.1010618

Eddelbuettel, D. and Francois, R. (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1–18. https://doi.org/10.18637/jss.v040.i08

Ferretti, L. et al. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936. https://doi.org/10.1126/science.abb6936

Bui LV, Nguyen HT, Levine H, Nguyen HN, Nguyen T-A, Nguyen TP, et al. (2020) Estimation of the incubation period of COVID-19 in Vietnam. PLoS ONE 15(12): e0243889. https://doi.org/10.1371/journal.pone.0243889

Paper: 'EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number'

Wed, 19 Oct 2022 00:00:00 +0000

Introduction

A colleague (and friend) of mine recently published a research paper entitled “EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number” in PLoS Computational Biology.

I am not in the habit of sharing research paper to which I did not contribute. Nevertheless, I would like to make an exception with this one because I strongly believe that the method developed in the paper deserves to be known, especially for anyone working in epidemiology.

Below is the motivation behind the article, as well as an illustration on simulated and real data (US hospitalization data). More information can be found in the paper and on the accompanying website.

Motivation

EpiLPS (Gressani et al. 2022) is a methodology for flexible Bayesian inference of the time-varying reproduction number $\mathcal{R}_t$; the average number of secondary cases generated by an infected agent at time $t$. This is a key epidemiological parameter that informs about the transmission potential of an infectious disease and can be used by public health authorities to gauge the effectiveness of interventions and propose an orientation for future control strategies.

This metric has gained in popularity during the SARS-CoV-2 pandemic with wide media coverage as its meaning is easily and intuitively grasped. Put simply, when $\mathcal{R} < 1$, the signal is encouraging as the epidemic is under control and will eventually vanish. On the contrary, a value of $\mathcal{R} > 1$ means that the disease keeps spreading and infections are witnessing an expansionary impact. Having a robust and reliable tool to compute the reproduction number from infectious disease data is therefore crucial.

A group of researchers in the EpiPose team from Hasselt University (Belgium), Leiden University (The Netherlands), and the University of Bern (Switzerland) have recently developed a new methodology for estimating the instantaneous reproduction number from incidence time series data for a given serial interval distribution (the time elapsed between the onset of symptoms in an infector and the onset of symptoms of secondary cases).

They termed their approach EpiLPS for “Epidemiological modeling with Laplacian-P-Splines” as Laplace approximations and P-splines smoothers are key ingredients that form the backbone of the proposed methodology.

The EpiLPS model assumes that the observed reported cases (by reporting date or date of symptom onset) are governed by a negative binomial distribution. As such, it allows to take the feature of overdispersion into account, contrary to a Poisson model. The epidemic curve is smoothed with P-splines (where posterior estimates of latent variables are computed via Laplace approximations) in a first step and a renewal equation model is used in a second step as a bridge between the reproduction number and the estimated spline coefficients through a “plug-in” method.

The authors also explain the main difference between EpiLPS and EpiEstim, a well established approached for estimating $\mathcal{R}_t$ in real-time developed by Cori et al. (2013) and make extensive comparisons between the two approaches under different epidemic scenarios.

An interesting feature of EpiLPS is that the user can choose between a fully “sampling-free” path, where model hyperparameters are fixed at their maximum a posteriori (LPSMAP) or a fully stochastic path (LPSMALA) based on a Metropolis-adjusted Langevin algorithm (LPSMALA). Talking about efficiency, routines for Laplace approximations and B-splines evaluations have been coded in C++ and integrated in R via the Rcpp package, so that the underlying algorithm can be executed in negligible time.

Below, we provide a short example of how to use the EpiLPS routines to estimate $\mathcal{R}_t$.

Getting started

The EpiLPS package is available from CRAN (see https://cran.r-project.org/web/packages/EpiLPS/index.html) and can be installed from the R console by typing:

install.packages("EpiLPS")

The package can then be loaded as follows:

library("EpiLPS")

The EpiLPS package structure is fairly simple as it consists in a few routines:

The function epilps() is the core routine for fitting the reproduction number.
With plot.epilps(), the user can plot the estimated epidemic curve and $\mathcal{R}_t$.
Finally, two ancillary routines, episim() and perfcheck() have been developed to essentially reproduce the simulation results of the associated paper.

A simulated example

A set of epidemic data can be simulated with the episim() routine by specifying a serial interval distribution and by choosing among a set of available patterns for the true reproduction number curve (here we choose pattern number 5 corresponding to a rather wiggly curve).

The simulated outbreak is for a duration of 40 days as specified in the endepi option. By setting the option plotsim = TRUE, the routine returns a figure summarizing the incidence time series, a bar plot for the specified serial interval distribution and the true underlying reproduction number curve.

set.seed(1234)

SI <- c(0.344, 0.316, 0.168, 0.104, 0.068)
simepidemic <- episim(
  serial_interval = SI,
  Rpattern = 5,
  plotsim = TRUE,
  verbose = TRUE,
  endepi = 40
)

## Chosen scenario: 5 'Wiggly then stable Rt'.
## Incidence of cases generated from a Poisson distribution. 
## Total number of days of epidemic: 40.

If you want to have an overview of the generated incidence time series, just type:

simepidemic$y

##  [1]  10   6  15  24  37  43  54  46  47  28  20   8  10  10   3   5   3   4   6
## [20]   6  15  21  44  75 135 217 329 409 453 487 457 443 297 290 255 246 246 339
## [39] 395 573

Smoothing the epidemic curve and estimating $\mathcal{R}_t$

Let us now use the epilps() routine to smooth the epidemic curve and estimate the reproduction number.

We will do this through LPSMAP (a fully sampling-free approach) and via LPSMALA (a fully stochastic approach relying on a MCMC algorithm with Langevin dynamics), where we specify a chain of length 10000 and a burn-in of size 4000.

LPSMAP_fit <- epilps(
  incidence = simepidemic$y,
  serial_interval = SI,
  tictoc = TRUE
)

## Inference method chosen: LPSMAP. 
## CI for LPSMAP computed via lognormal posterior approx. of Rt.Total number of days: 40. 
## Mean Rt discarding first 7 days: 1.327.
## Mean 95% CI of Rt discarding first 7 days: (1.164,1.527) 
## Elapsed real time (wall clock time): 0.261 seconds.

LPSMALA_fit <- epilps(
  incidence = simepidemic$y, serial_interval = SI,
  method = "LPSMALA", chain_length = 10000, burn = 4000
)

## Inference method chosen: LPSMALA with chain length 10000 and warmup 4000.
## MCMC acceptance rate: 56.41%. 
## Geweke z-score < 2.33 for:  32 / 33  variables. 
## Total number of days: 40. 
## Mean Rt discarding first 7 days: 1.326.
## Mean 95% CI of Rt discarding first 7 days: (1.117,1.555). 
## Timing of routine not requested.

After execution, each routine prints in the console a brief summary of the method that has been requested by the user.

For LPSMALA, it summarizes the chain length, the acceptance rate (should be around 56%) and other basic information. As can be seen from the printed output, the mean reproduction number for the simulated epidemic is around 1.33.

We can now use, say, the LPSMALA_fit object together with the plot() routine to obtain the smoothed epidemic curve and the estimated reproduction number (by default the credible interval is at a 5% level of significance but this can be changed by the user).

days <- seq(8, 40)

#--- Smoothed epidemic curve
gridExtra::grid.arrange(
  plot(LPSMALA_fit,
    plotout = "epicurve", incibars = TRUE, themetype = "light",
    epicol = "darkgreen", cicol = rgb(0.3, 0.73, 0.3, 0.2),
    epititle = "Smoothed epidemic curve", titlesize = 13, barwidth = 0.25
  ),

  #--- Estimated reproduction number
  plot(LPSMALA_fit,
    plotout = "rt", theme = "light", rtcol = "black",
    titlesize = 13, Rtitle = "Estimated R (LPSMALA)"
  ),
  nrow = 1, ncol = 2
)

The figure can be customized in various ways:

Users can specify the theme under themetype. Available options are gray (the default), classic, light and dark.
Other choices, such as whether or not to show the incidence bars, the color of the credible interval envelope, the color of the smoothed epidemic curve and the estimated reproduction number are also available.

The figure above was generated within the ggplot2 package, but there is also another way of extracting information directly from the LPSMAP_fit and LPSMALA_fit objects. In fact, the estimated reproduction number values and their associated credible interval for each day can be extracted and plotted.

Below, we make the exercise and plot the estimated $\mathcal{R}_t$ obtained with LPSMAP and LPSMALA, respectively and compare it with the true underlying reproduction number curve. The fit is quite good.

par(mfrow = c(1, 2))

#--- LPSMAP vs target R
plot(days, sapply(days, simepidemic$Rtrue),
  type = "l", lwd = 2, ylim = c(0, 4),
  ylab = "Estimated R", xlab = "Time"
)
polygon(
  x = c(days, rev(days)), y = c(
    LPSMAP_fit$epifit$R95CI_low[8:40],
    rev(LPSMAP_fit$epifit$R95CI_up[8:40])
  ),
  col = rgb(0.23, 0.54, 1, 0.3), border = NA
)
lines(days, LPSMAP_fit$epifit$R_estim[8:40], type = "l", col = "cornflowerblue", lwd = 2)
lines(days, sapply(days, simepidemic$Rtrue), type = "l", lwd = 2)

grid(nx = 10, ny = 10)
legend("topright",
  lty = c(1, 1), lwd = c(2, 2),
  col = c("black", "blue", rgb(0.23, 0.54, 1, 0.3)),
  c("Target R", "LPSMAP", "LPSMAP 95% CI"), bty = "n", cex = 0.9
)

#--- LPSMALA vs target R
plot(days, sapply(days, simepidemic$Rtrue),
  type = "l", lwd = 2, ylim = c(0, 4),
  ylab = "Estimated R", xlab = "Time"
)
polygon(
  x = c(days, rev(days)), y = c(
    LPSMALA_fit$epifit$R95CI_low[8:40],
    rev(LPSMALA_fit$epifit$R95CI_up[8:40])
  ),
  col = rgb(1, 0.23, 0.31, 0.3), border = NA
)
lines(days, LPSMALA_fit$epifit$R_estim[8:40], type = "l", col = "red", lwd = 2)
lines(days, sapply(days, simepidemic$Rtrue), type = "l", lwd = 2)

grid(nx = 10, ny = 10)
legend("topright",
  lty = c(1, 1), lwd = c(2, 2),
  col = c("black", "red", rgb(1, 0.23, 0.31, 0.3)),
  c("Target R", "LPSMALA", "LPSMALA 95% CI"), bty = "n", cex = 0.9
)

You can access, say, the results of the last week of the epidemic by typing:

# Estimated R of the last week (with LPSMAP)
round(tail(LPSMAP_fit$epifit[, 1:4], 7), 3)

##    Date R_estim R95CI_low R95CI_up
## 34   34   0.708     0.663    0.756
## 35   35   0.724     0.676    0.775
## 36   36   0.809     0.755    0.868
## 37   37   0.971     0.908    1.039
## 38   38   1.205     1.135    1.279
## 39   39   1.461     1.384    1.541
## 40   40   1.671     1.557    1.794

# Estimated mean number of cases of the last week (with LPSMAP)
round(tail(LPSMAP_fit$epifit[, 5:7], 7))

##    mu_estim mu95CI_low mu95CI_up
## 34      284        225       357
## 35      254        202       319
## 36      248        196       312
## 37      267        211       338
## 38      319        253       404
## 39      411        325       520
## 40      552        394       774

USA hospitalization data

To illustrate EpiLPS on real data, we download hospitalization data from the COVID19 package for the USA in the period ranging from 2021-09-01 to 2022-09-01 and apply the epilps() routine to estimate the reproduction number.

install.packages("COVID19")
library("COVID19")

# Get data and specify serial interval distribution
USADat <- COVID19::covid19(
  country = "US", level = 1, start = "2021-09-01",
  end = "2022-09-01", verbose = FALSE
)

si <- c(0.344, 0.316, 0.168, 0.104, 0.068)

inciUSA <- USADat$hosp
dateUSA <- USADat$date

We use the epilps() routine with method LPSMAP (default) and plot the smoothed epidemic curve and the estimated reproduction number with a 95% credible interval.

epifit <- epilps(incidence = inciUSA, serial_interval = si, K = 20)

## Inference method chosen: LPSMAP. 
## CI for LPSMAP computed via lognormal posterior approx. of Rt.Total number of days: 366. 
## Mean Rt discarding first 7 days: 0.994.
## Mean 95% CI of Rt discarding first 7 days: (0.983,1.005) 
## Timing of routine not requested.

gridExtra::grid.arrange(
  plot(epifit,
    dates = dateUSA, datelab = "3m",
    plotout = "epicurve", incibars = FALSE, themetype = "light",
    epicol = "darkgreen", cicol = rgb(0.3, 0.73, 0.3, 0.2),
    epititle = "USA smoothed epidemic curve", titlesize = 13
  ),
  plot(epifit,
    dates = dateUSA, datelab = "3m",
    plotout = "rt", theme = "light", rtcol = "black",
    titlesize = 13, Rtitle = "USA Estimated R"
  ),
  nrow = 1, ncol = 2
)

Conclusion

Thanks for reading.

I hope you will find the method developed in the paper as useful as I do. Feel free to reach out to me and to the authors if you happen to use it for your own research.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

References

Cori, Anne, Neil M Ferguson, Christophe Fraser, and Simon Cauchemez. 2013. “A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics.” American Journal of Epidemiology 178 (9): 1505–12.

Gressani, Oswaldo, Jacco Wallinga, Christian L Althaus, Niel Hens, and Christel Faes. 2022. “EpiLPS: A Fast and Flexible Bayesian Tool for Estimation of the Time-Varying Reproduction Number.” PLoS Computational Biology 18 (10): e1010618. https://doi.org/10.1371/journal.pcbi.1010618.

COVID-19 in Belgium: is it over yet?

Fri, 22 May 2020 00:00:00 +0000

Introduction

Note 1: The present article has been written on May 22, 2020 and has been updated infrequently. The current situation regarding COVID-19 in Belgium may therefore be different to what is presented below. See my Twitter profile for more frequent updates of the plots.

Note 2: This is a joint work with Prof. Niko Speybroeck, Prof. Catherine Linard, Prof. Simon Dellicour and Angel Rosas-Aguirre.

Belgium recently started to lift its lockdown measures initially imposed to contain the spread of the Covid-19. Following this decision taken by Belgian authorities, we analyze how the situation evolved so far.

Contrarily to a previous article in which I analyzed the outbreak of the Coronavirus in Belgium using the SIR model, in this article we focus on the evolution of the number of:

hospital admissions
patients in hospitals
patients in intensive care
new confirmed cases

at the province and national level.

Data is from Sciensano and all plots were created with the {ggplot2} package.

New hospital admissions

Overall

Belgian hospitalizations COVID-19

From the above figure, we see that the rate of hospitalizations continue with a decreasing trend in all provinces (and in Belgium as well).

Download the figure, or see the code.

Update of October 27, 2020:

COVID19 hospitalizations in Belgium

Download the figure, or see the code.

The detailed situation in Brabant:

Download the figure, or see the code.

By period

Daily COVID19 hospitalizations in Belgium from March to October 2020

Download the figure, or see the code.

Update of November 16, 2020:

Daily COVID19 hospitalizations in Belgium by period

Download the figure.

In the first wave, the province of Limburg recorded on average the highest number of COVID19 hospital admissions per million inhabitants. During the second wave, Liège and Hainaut struggled with the highest rates. With two exceptions (Antwerp and Limburg), last month was worse than in March-April. In three provinces (Hainaut, Namur and Liège), the number has more than doubled.

During the period from June 14 to July 15, 2020, the number of COVID19 hospital admissions in Belgium fell to very low relative levels, but we have failed to maintain them. Now that hospital admissions are no longer increasing, we hope that the colors will lighten up again a bit as the end of the year approaches.

Zooming in

Hospital admissions COVID-19 - Belgium

Download the figure or see the code.

Update of February 26, 2021:

There is some ongoing debate in Belgium on whether or not to ease restrictions. On February 26, 2021, Belgian authorities will meet, discuss, debate and decide. Current levels and trends of COVID-19 hospitalizations may guide them:

There is still no strong evidence that COVID-19 hospitalization curves decrease in Belgium. The comparison between the first (in gray - dates & curve) and second wave (in blue - dates & curve) needs to be done with care, but indicates that current hospitalization levels are not as low as some may like:

Zooming in provides some additional insights on the COVID-19 levels during the first and second waves at province level:

This shows that the second wave resulted in more hospitalizations than the first one in most Belgian provinces, despite the warning of a first deadly wave. It also illustrates the fact that daily hospitalizations in Belgium are currently still higher than what was observed at the end of the first wave.

Put simply, the bad news is that the combination of the number of contacts and the risk of transmission by contact seems (at the moment) not sufficiently low to result in a considerable decrease of hospitalizations. Yet (put simply), the good news today is that there is already some immunity in the population and that vaccinations may increase this immunity considerably. This can help in pushing curves down. Let’s not lose hope.

Download figures (1, 2 and 3) or see the code.

Update of May 10, 2021:

COVID19 hospitalizations - Wave 1 and 2

When looking at the above plot, bad news are that:

current levels correspond to levels of October 2020 and
current levels are still about double the target of 75 hospitalizations per day.

There are, however, three good news (compared to October 2020):

decreasing curve,
vaccination and
good weather.

Update of June 4, 2021

COVID-19 hospitalizations in Belgium below 75/day

The good news is that the number of COVID-19 hospitalizations in Belgium is now below the well-known threshold of 75 hospitalizations per day (which is a target defined by the Belgian government). This is the way to go, and we hope this trend will continue in the coming days/weeks.

Patients in hospitals

Below the evolution of the number of patients in hospitals in Belgium:

COVID19 patients in hospitals in Belgium

Download the figure or see the code.

We see that, as of October 28, 2020, the number of COVID19 patients in Belgian hospitals reached the peak of the first wave. So although patients stay shorter at the hospital during the second wave compared to the first wave, hospitals are still getting crowded.

Therefore, if the number of patients in hospitals follows the same path in the coming weeks, hospitals will quickly become too crowded and will not be able to accept new patients as their maximum capacity will soon be reached (if this is not already the case…).

Patients in intensive care

Below the evolution of COVID19 patients in intensive care in Belgium, with short-term projections and 99% confidence interval:

Evolution of COVID19 patients in intensive care in Belgium

Download the figure.

Short-term projections indicate what may have happened without the slow-down in transmission. This slow-down is positive news.

The maps show total intensive care patients by province if these would have had the Belgian population. Map at the top shows maximum levels in March-April and map at the bottom shows current levels. The maps indicate high intensive care use due to COVID19. In most Belgian provinces, numbers are still higher today than March-April peak numbers.

Observations are in line with other preliminary indications, such as trends of COVID19 hospitalizations (currently relatively volatile), indicating that transmission is slowing down:

Download the figure.

Confirmed cases

Note that the reported number of new confirmed cases is probably underestimated. This number does not take into account undiagnosed (without or with few symptoms) or untested cases. Therefore, figures with number of cases should be interpreted with extreme caution.

By province

New confirmed COVID-19 cases in Belgium

Download the figure or see the code.

By age group and sex

Static

Below another visualization of the number of cases by age group and sex in Belgium, for three different periods:

COVID-19 cases by age group and sex in Belgium

Download the figure or see the code.

This visualization shows the importance to report ages of cases and not just total number.

Moreover, we see that the distribution of cases per week by age group at the beginning of September is similar than during the summer holidays, but the number of cases per week is higher. The distribution of cases per week by age group at the beginning of September is however different from the “first wave” (period from March 1, 2020 to May 31, 2020). During the fist period, majority of cases were elderly, while at the beginning of September majority of cases are young people. It would be interesting to see how the distribution of cases by age group evolves during winter.

The figure above may be put in relation with the structure of the Belgian population:

Structure of Belgian population (2019)

Download the figure or see the code.

Dynamic

Additionally, these can be seen dynamically:

COVID-19 cases by age group and sex in Belgium - dynamic version

Download the figure or see the code.

With an update of the second wave:

Age and sex specific incidence per 100 000 of COVID19 cases in Belgium - dynamic version

Download the figure or see the code.

By age group, sex and province

Download the figure or see the code.

Conclusion

Thanks for reading.

We hope that these figures will evolve in the right direction. In the meantime, take care and stay safe!

If you would like to be further updated on the evolution of the COVID-19 epidemic, two options:

visit the blog from time to time, and
join Twitter and follow us: statsandr, NikoSpeybroeck & arosas_aguirre

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

A package to download free Springer books during Covid-19 quarantine

Sun, 26 Apr 2020 00:00:00 +0000

Update

The promotion has ended so it is not possible to download the books through R. If you did not download the books in time, you can still have access to them via this link.¹

Introduction

You probably already have seen that Springer released about 500 books for free following the COVID-19 pandemic. According to Springer, these textbooks will be available free of charge until at least the end of July.

Following this announcement, I already downloaded a couple of statistics and R programming textbooks from their website and I will probably download a few more in the coming weeks.

In this article, I present a package that saved me a lot of time and which may be of interest to many of us: the {springerQuarantineBooksR} package, developed by Renan Xavier Cortes.

This package allows you to easily download all (or a selection of) Springer books made available free of charge during the COVID-19 quarantine.

With this large collection of high quality resources and my collection of top R resources about the Coronavirus, we do not have any excuse to not read and learn during this quarantine.

In this article, I show:

how to download all available textbooks at once and
how to download a subset of books, given a specific title, author or subject

Without further ado, here is how the package works in practice.

Installation

After having installed the {devtools} package, you can install the {springerQuarantineBooksR} package from GitHub and load it with:

# install.packages("devtools")
devtools::install_github("renanxcortes/springerQuarantineBooksR", force = TRUE)
library(springerQuarantineBooksR)

Download all books at once

First, set the path where you would like to save all books with the setwd() function then download all of them at once with the download_springer_book_files() function. Note that it takes several minutes (depending on the speed of your internet connection) since all books combined amount for almost 8GB.

setwd("path_of_your_choice") # where you want to save the books
download_springer_book_files() # download all of them at once

You will find all downloaded books (in PDF format) in a folder named “springer_quarantine_books”, organized by category.²

If you want to download the EPUB version (or both the PDF and EPUB versions), add the filetype argument to the function:

# for EPUB version:
download_springer_book_files(filetype = "epub")

# for both PDF and EPUB versions:
download_springer_book_files(filetype = "both")

By default, it downloads only the English books. However, it is also possible to download all German books by adding the argument lan = 'ger':

download_springer_book_files(lan = "ger")

Note that in total, there are 407 unique titles in English and 52 in German.

Create a table of Springer books

Like me, if you do not know which books are offered by Springer and you do not want to download all of them, you probably may want to have an overview or a list of the released books before downloading any.

For this, you can load a table containing all the titles made available by Springer into an R session with the download_springer_table() function:

springer_table <- springerQuarantineBooksR::download_springer_table()

This table can then be improved with the {DT} package to:

keep only a minimum of information,
allow searching a book by its title, author, classification or year,
allow downloading the list of available books, and
make the Springer links clickable for instance

# install.packages("DT")
library(DT)

springer_table$open_url <- paste0(
  '<a target="_blank" href="', # opening HTML tag
  springer_table$open_url, # href link
  '">SpringerLink</a>' # closing HTML tag
)

springer_table <- springer_table[, c(1:3, 19, 20)] # keep only relevant information

datatable(springer_table,
  rownames = FALSE, # remove row numbers
  filter = "top", # add filter on top of columns
  extensions = "Buttons", # add download buttons
  options = list(
    autoWidth = TRUE,
    dom = "Blfrtip", # location of the download buttons
    buttons = c("copy", "csv", "excel", "pdf", "print"), # download buttons
    pageLength = 5, # show first 5 entries, default is 10
    order = list(0, "asc") # order the title column by ascending order
  ),
  escape = FALSE # make URLs clickable
)

This table allows you to see which textbooks Springer offers (together with some information) and allows you to find the ones that you are most likely to be interested in.

Note that you can create a similar table for German books with the download_springer_table(lan = "ger") function.

Download only specific books

Now that you have a better idea about the books you are interested in, you can download them by their title, author or subject.

By title

Say that you are interested in downloading only one specific book and you know its title. For instance, suppose you want to download the book entitled “All of Statistics”:

download_springer_book_files(springer_books_titles = "All of Statistics")

If you are interested to download more than one book, run the following command:

download_springer_book_files(
  springer_books_titles = c(
    "All of Statistics",
    "A Modern Introduction to Probability and Statistics"
  )
)

Alternatively, if you do not have a specific title in mind but you are interested in downloading all books with the word “Statistics” in the title, you can run:

springer_table <- download_springer_table()

library(dplyr)
specific_titles_list <- springer_table %>%
  filter(str_detect(
    book_title, # look for a pattern in the book_title column
    "Statistics" # specify the word
  )) %>%
  pull(book_title)

download_springer_book_files(springer_books_titles = specific_titles_list)

Tip: If you would like to download all books with the word “Statistics” or “Data Science” in the title, replace "Statistics" in the above code by "Statistics|Data Science".

By author

If you want to download all books from a specific author, you can run:

springer_table <- download_springer_table()

# library(dplyr)
specific_titles_list <- springer_table %>%
  filter(str_detect(
    author, # look for a pattern in the author column
    "John Hunt" # specify the author
  )) %>%
  pull(book_title)

download_springer_book_files(springer_books_titles = specific_titles_list)

By subject

You can also download all textbooks covering a specific subject (see all subjects in the subject_classification column in the summary table). For instance, here is how to download all books categorized in the Statistics subject:

springer_table <- download_springer_table()

# library(dplyr)
specific_titles_list <- springer_table %>%
  filter(str_detect(
    subject_classification, # look for a pattern in the subject_classification column
    "Statistics" # specify the subject
  )) %>%
  pull(book_title)

download_springer_book_files(springer_books_titles = specific_titles_list)

Improvements

Below a list of features that can potentially be implemented in order to improve the package:

Add the possibility to download all editions of a book. Currently, only the latest edition can be downloaded.
Add the possibility to resume downloading if it stopped. Currently, if the code is executed again, the downloads start from scratch.
Add the possibility of downloading books by topic. Currently, it is only possible by title, author or subject.

Feel free to open a pull request on GitHub if you have another improvement in mind.

Acknowledgments

I would like to thank:

Renan Xavier Cortes (and all contributors) for providing this package
The springer_free_books Python project which was used as inspiration to the {springerQuarantineBooksR} package
And last but not least, Springer who offers many of their excellent books for free!

Conclusion

Thanks for reading.

I hope this article will help you to download and read more high quality materials made available by Springer during this Covid-19 quarantine.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Note that I am not the author nor the maintainer of this shared folder. Therefore, I do not know how long the books will be available through this link, and I am not responsible if some (or all) books are removed.↩︎
Note that you can change the folder name by specifying the argument destination_folder = "name_of_your_choice".↩︎

COVID-19 in Belgium

Tue, 31 Mar 2020 00:00:00 +0000

Introduction

The Novel COVID-19 Coronavirus is still spreading quickly in several countries and it does not seem like it is going to stop anytime soon as the peak has not yet been reached in many countries.

Since the beginning of its expansion, a large number of scientists across the world have been analyzing this Coronavirus from different perspectives and with different technologies with the hope of coming up with a cure in order to stop its expansion and limit its impact on citizens.

Top R resources on Coronavirus

In the meantime, epidemiologists, statisticians and data scientists are working towards a better understanding of the spread of the virus in order to help governments and health agencies in taking the most optimal decisions. This led to the publication of a great deal of online resources about the virus, which I collected and organized in an article covering the top R resources on Coronavirus. This article is a collection of the best resources I’ve had the chance to discover, with a brief summary for each of them. It includes Shiny apps, dashboards, R packages, blog posts and datasets.

Publishing this collection led many readers to submit their piece of work, which made the article even more complete and more insightful for anyone interested in analyzing the virus from a quantitative perspective. Thanks to everyone who contributed and who helped me in collecting and summarizing these R resources about COVID-19!

Given my field of expertise, I am not able to help in this fight against the virus from a medical point of view. However, I still wanted to contribute as much as I could. From understanding better the disease to bringing scientists and doctors together to build something bigger and more impactful, I truly hope that this collection will, to a small extent, help to fight the pandemic.

Coronavirus dashboard for your own country

Besides receiving analyses, blog posts, R code and Shiny apps from people across the world, I realized that many people were trying to create a dashboard tracking the spread of the Coronavirus for their own country. So in addition to the collection of top R resources, I also published an article detailing the steps to follow to create a dashboard specific to a country. See how to create such dashboard in this article and an example with Belgium.

The code has been made available on GitHub and is open source so everyone can copy it and adapt it to their own country. The dashboard was intentionally kept simple so anyone with a minimum knowledge in R could easily replicate it, and advanced users could enhance it according to their needs.

Motivations, limitations and structure of the article

By seeing and organizing many R resources about COVID-19, I am fortunate enough to have read a lot of excellent analyses on the disease outbreak, the impact of different health measures, forecasts of the number of cases, projections about the length of the pandemic, hospitals capacity, etc.

Furthermore, I must admit that some countries such as China, South Korea, Italy, Spain, UK and Germany received a lot of attention as shown by the number of analyses done on these countries. However, to my knowledge and at the date of publication of this article, I am not aware of any analysis of the spread of the Coronavirus specifically for Belgium.¹ The present article aims at filling that gap.

Throughout my PhD thesis in statistics, my main research interest is about survival analysis applied to cancer patients (more information in the research section of my personal website). I am not an epidemiologist and I have no extensive knowledge in modelling disease outbreaks via epidemiological models.

I usually write articles only about things I consider myself familiar with, mainly statistics and its applications in R. At the time of writing this article, I was however curious where Belgium stands regarding the spread of this virus, I wanted to play with this kind of data in R (which is new to me) and see what comes out.

In order to satisfy my curiosity while not being an expert, in this article I am going to replicate analyses done by more knowledgeable people and apply them to my country, that is, Belgium. From all the analyses I have read so far, I decided to replicate the analyses done by Tim Churches and Prof. Dr. Holger K. von Jouanne-Diedrich. This article is based on a mix of their articles which can be found here and here. They both present a very informative analysis on how to model the outbreak of the Coronavirus and show how contagious it is. Their articles also allowed me to gain an understanding of the topic and in particular an understanding of the most common epidemiological model. I strongly advise interested readers to also read their more recent articles for more advanced analyses and for an even deeper understanding of the spread of the COVID-19 pandemic.

Other more complex analyses are possible and even preferable, but I leave this to experts in this field. Note also that the following analyses take into account only the data until the date of publication of this article, so the results should not be viewed, by default, as current findings.

In the remaining of the article, we first introduce the model which will be used to analyze the Coronavirus outbreak in Belgium. We also briefly discuss and show how to compute an important epidemiological measure, the reproduction number. We then use our model to analyze the outbreak of the disease in the case where there would be no public health intervention. We conclude the article by summarizing more advanced tools and techniques that could be used to further model COVID-19 in Belgium.

Analysis of Coronavirus in Belgium

A classic epidemiological model: the SIR model

Before diving into the real-life application, we first introduce the model that will be used.

There are many epidemiological models but we will use one of the most common one, the SIR model. The SIR model can be complexified to incorporate more specificities of the virus outbreak, but in this article we keep its simplest version. Tim Churches’ explanation of this model and how to fit it using R is so nice, I will reproduce it here with a few minor changes.

The basic idea behind the SIR model (Susceptible - Infectious - Recovered) of communicable disease outbreaks is that there are three groups (also called compartments) of individuals:

S: those who are healthy but susceptible to the disease (i.e., at risk of being contaminated). At the start of the pandemic, S is the entire population since no one is immune to the virus.
I: the infectious (and thus, infected) people
R: individuals who were contaminated but who have either recovered or died. They are not infectious anymore.

These groups evolve over time as the virus progresses in the population:

S decreases when individuals are contaminated and move to the infectious group I
As people recover or die, they go from the infected group I to the recovered group R

To model the dynamics of the outbreak we need three differential equations to describe the rates of change in each group, parameterised by:

$\beta$, the infection rate, which controls the transition between S and I
$\gamma$, the removal or recovery rate, which controls the transition between I and R

Formally, this gives:

\[\frac{dS}{dt} = - \frac{\beta IS}{N} \text{ (Eq. 1)}\]

\[\frac{dI}{dt} = \frac{\beta IS}{N} - \gamma I \text{ (Eq. 2)}\]

\[\frac{dR}{dt} = \gamma I \text{ (Eq. 3)}\]

The first equation (Eq. 1) states that the number of susceptible individuals (S) decreases with the number of newly infected individuals, where new infected cases are the result of the infection rate ($\beta$) multiplied by the number of susceptible individuals (S) who had a contact with infectious individuals (I).

The second equation (Eq. 2) states that the number of infectious individuals (I) increases with the newly infected individuals ($\beta I S$), minus the previously infected people who recovered (i.e., $\gamma I$ which is the removal rate $\gamma$ multiplied by the infectious individuals I).

Finally, the last equation (Eq. 3) states that the recovered group (R) increases with the number of individuals who were infectious and who either recovered or died ($\gamma I$).

An epidemic develops as follows:

Before the start of the disease outbreak, S equals the entire population as no one has anti-bodies.
At the beginning of the outbreak, as soon as the first individual is infected, S decreases by 1 and I increases by 1 as well.
This first infectious individual contaminates (before recovering or dying) other individuals who were susceptible.
The dynamic continues, with recently contaminated individuals who in turn infect other susceptible people before they recover.

Visually, we have:

SIR model. Source: Kai Sasaki.

Before fitting the SIR model to the data, the first step is to express these differential equations as an R function, with respect to time t.

SIR <- function(time, state, parameters) {
  par <- as.list(c(state, parameters))
  with(par, {
    dS <- -beta * I * S / N
    dI <- beta * I * S / N - gamma * I
    dR <- gamma * I
    list(c(dS, dI, dR))
  })
}

Fitting a SIR model to the Belgium data

To fit the model to the data we need two things:

a solver for these differential equations
an optimiser to find the optimal values for our two unknown parameters, $\beta$ and $\gamma$

The function ode() (for ordinary differential equations) from the {deSolve} R package makes solving the system of equations easy, and to find the optimal values for the parameters we wish to estimate, we can just use the optim() function built into base R.

Specifically, what we need to do is minimise the sum of the squared differences between $I(t)$, which is the number of people in the infectious compartment $I$ at time $t$, and the corresponding number of cases as predicted by our model $\hat{I}(t)$. This quantity is known as the residual sum of squares (RSS):

\[RSS(\beta, \gamma) = \sum_t \big(I(t) - \hat{I}(t) \big)^2\]

In order to fit a model to the incidence data for Belgium, we need a value N for the initial uninfected population. The population of Belgium in November 2019 was 11,515,793 people, according to Wikipedia. We will thus use N = 11515793 as the initial uninfected population.

Next, we need to create a vector with the daily cumulative incidence for Belgium, from February 4 (when our daily incidence data starts), through to March 30 (last available date at the time of publication of this article). We will then compare the predicted incidence from the SIR model fitted to these data with the actual incidence since February 4. We also need to initialise the values for N, S, I and R. Note that the daily cumulative incidence for Belgium is extracted from the {coronavirus} R package developed by Rami Krispin.

# devtools::install_github("RamiKrispin/coronavirus")
library(coronavirus)
data(coronavirus)

`%>%` <- magrittr::`%>%`

# extract the cumulative incidence
df <- coronavirus %>%
  dplyr::filter(country == "Belgium") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )

# put the daily cumulative incidence numbers for Belgium from
# Feb 4 to March 30 into a vector called Infected
library(lubridate)

sir_start_date <- "2020-02-04"
sir_end_date <- "2020-03-30"

Infected <- subset(df, date >= ymd(sir_start_date) & date <= ymd(sir_end_date))$active_cum

# Create an incrementing Day vector the same length as our
# cases vector
Day <- 1:(length(Infected))

# now specify initial values for N, S, I and R
N <- 11515793
init <- c(
  S = N - Infected[1],
  I = Infected[1],
  R = 0
)

Note that what is needed are currently infected persons (cumulative infected minus the removed, i.e. recovered or dead). However, numbers of recovered persons are hard to obtain and probably underestimated due to underreporting bias. I thus consider the cumulative number of infected people, which is probably not an issue here since the number of recovered cases is negligible at the time of the analysis.

Then we need to define a function to calculate the RSS, given a set of values for $\beta$ and $\gamma$.

# define a function to calculate the residual sum of squares
# (RSS), passing in parameters beta and gamma that are to be
# optimised for the best fit to the incidence data
RSS <- function(parameters) {
  names(parameters) <- c("beta", "gamma")
  out <- ode(y = init, times = Day, func = SIR, parms = parameters)
  fit <- out[, 3]
  sum((Infected - fit)^2)
}

Finally, we can fit the SIR model to our data by finding the values for $\beta$ and $\gamma$ that minimise the residual sum of squares between the observed cumulative incidence (observed in Belgium) and the predicted cumulative incidence (predicted by our model). We also need to check that our model has converged, as indicated by the message shown below:

# now find the values of beta and gamma that give the
# smallest RSS, which represents the best fit to the data.
# Start with values of 0.5 for each, and constrain them to
# the interval 0 to 1.0

# install.packages("deSolve")
library(deSolve)

Opt <- optim(c(0.5, 0.5),
  RSS,
  method = "L-BFGS-B",
  lower = c(0, 0),
  upper = c(1, 1)
)

# check for convergence
Opt$message

## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"

Convergence is confirmed. Note that you may find different estimates for different choices of initial values or constraints. This proves that the fitting process is not stable. Here is a potential solution for a better fitting process.

Now we can examine the fitted values for $\beta$ and $\gamma$:

Opt_par <- setNames(Opt$par, c("beta", "gamma"))
Opt_par

##      beta     gamma 
## 0.5841185 0.4158816

Remember that $\beta$ controls the transition between S and I (i.e., susceptible and infectious) and $\gamma$ controls the transition between I and R (i.e., infectious and recovered). However, those values do not mean a lot but we use them to get the fitted numbers of people in each compartment of our SIR model for the dates up to March 30 that were used to fit the model, and compare those fitted values with the observed (real) data.

# time in days for predictions
t <- 1:as.integer(ymd(sir_end_date) + 1 - ymd(sir_start_date))

# get the fitted values from our SIR model
fitted_cumulative_incidence <- data.frame(ode(
  y = init, times = t,
  func = SIR, parms = Opt_par
))

# add a Date column and the observed incidence data
library(dplyr)
fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
  mutate(
    Date = ymd(sir_start_date) + days(t - 1),
    Country = "Belgium",
    cumulative_incident_cases = Infected
  )

# plot the data
library(ggplot2)
fitted_cumulative_incidence %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = I), colour = "red") +
  geom_point(aes(y = cumulative_incident_cases), colour = "blue") +
  labs(
    y = "Cumulative incidence",
    title = "COVID-19 fitted vs observed cumulative incidence, Belgium",
    subtitle = "(Red = fitted from SIR model, blue = observed)"
  ) +
  theme_minimal()

From the above graph we see that the number of observed confirmed cases follows (unfortunately) the number of confirmed cases expected by our model. The fact that both trends are overlapping indicates that the pandemic is clearly in an exponential phase in Belgium. More data would be needed to see whether this trend is confirmed in the long term.

The following graph is similar than the previous one, except that the y-axis is measured on a log scale. This kind of plot is called a semi-log plot or more precisely a log-linear plot because only the y-axis is transformed with a logarithm scale. Transforming the scale in log has the advantage that it is more easily readable in terms of difference between the observed and expected number of confirmed cases and it also shows how the number of observed confirmed cases differs from an exponential trend.

fitted_cumulative_incidence %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = I), colour = "red") +
  geom_point(aes(y = cumulative_incident_cases), colour = "blue") +
  labs(
    y = "Cumulative incidence",
    title = "COVID-19 fitted vs observed cumulative incidence, Belgium",
    subtitle = "(Red = fitted from SIR model, blue = observed)"
  ) +
  theme_minimal() +
  scale_y_log10(labels = scales::comma)

The plot indicates that, at the beginning of the pandemic and until March 12, the number of confirmed cases stayed below what would be expected in an exponential phase. In particular, the number of confirmed cases stayed constant at 1 case from February 4 to February 29. From March 13 and until March 30, the number of confirmed cases kept increasing at a rate close to an exponential rate.

We also notice a small jump between March 12 and March 13, which may potentially indicate an error in the data collection, or a change in the testing/screening methods.

Reproduction number $R_0$

Our SIR model looks like a good fit to the observed cumulative incidence data in Belgium, so we can now use our fitted model to calculate the basic reproduction number $R_0$, also referred as basic reproduction ratio, and which is closely linked to $\beta$ and $\gamma$.²

The basic reproduction number $R_0$ gives the average number of susceptible people who are infected by each infectious person where all individuals are susceptible to infection. In other words, the reproduction number refers to the number of healthy people that get infected per number of sick people. When $R_0 > 1$ the disease starts spreading in a population, but not if $R_0 < 1$. Usually, the larger the value of $R_0$, the harder it is to control the epidemic and the higher the probability of a pandemic.

Formally, we have:

\[R_0 = \frac{\beta}{\gamma}\]

We can compute it in R:

Opt_par

##      beta     gamma 
## 0.5841185 0.4158816

R0 <- as.numeric(Opt_par[1] / Opt_par[2])
R0

## [1] 1.404531

An $R_0$ of 1.4 is below values found by others for COVID-19 and the $R_0$ for SARS and MERS, which are similar diseases also caused by coronavirus. Furthermore, in the literature, it has been estimated that the reproduction number for COVID-19 is approximately 2.7 (with $\beta$ close to 0.54 and $\gamma$ close to 0.2). Our reproduction number being lower is mainly due to the fact that the number of confirmed cases stayed constant and equal to 1 at the beginning of the pandemic.

A $R_0$ of 1.4 means that, on average in Belgium, 1.4 persons are infected for each infected person.

For simple models, the proportion of the population that needs to be effectively immunized to prevent sustained spread of the disease, known as the “herd immunity threshold”, has to be larger than $1 - \frac{1}{R_0}$ (Fine, Eames, and Heymann 2011).

The reproduction number of 1.4 we just calculated suggests that, given the formula 1 - (1 / 1.4), 28.8% of the population should be immunized to stop the spread of the infection. With a population in Belgium of approximately 11.5 million, this translates into roughly 3.3 million people.

Using our model to analyze the outbreak if there was no intervention

It is instructive to use our model fitted to the first 56 days of available data on confirmed cases in Belgium, to see what would happen if the outbreak were left to run its course, without public health intervention.

# time in days for predictions
t <- 1:120

# get the fitted values from our SIR model
fitted_cumulative_incidence <- data.frame(ode(
  y = init, times = t,
  func = SIR, parms = Opt_par
))

# add a Date column and join the observed incidence data
fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
  mutate(
    Date = ymd(sir_start_date) + days(t - 1),
    Country = "Belgium",
    cumulative_incident_cases = c(Infected, rep(NA, length(t) - length(Infected)))
  )

# plot the data
fitted_cumulative_incidence %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = I), colour = "red") +
  geom_line(aes(y = S), colour = "black") +
  geom_line(aes(y = R), colour = "green") +
  geom_point(aes(y = cumulative_incident_cases),
    colour = "blue"
  ) +
  scale_y_continuous(labels = scales::comma) +
  labs(y = "Persons", title = "COVID-19 fitted vs observed cumulative incidence, Belgium") +
  scale_colour_manual(name = "", values = c(
    red = "red", black = "black",
    green = "green", blue = "blue"
  ), labels = c(
    "Susceptible",
    "Recovered", "Observed", "Infectious"
  )) +
  theme_minimal()

The same graph in log scale for the y-axis and with a legend for better readability:

# plot the data
fitted_cumulative_incidence %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = I, colour = "red")) +
  geom_line(aes(y = S, colour = "black")) +
  geom_line(aes(y = R, colour = "green")) +
  geom_point(aes(y = cumulative_incident_cases, colour = "blue")) +
  scale_y_log10(labels = scales::comma) +
  labs(
    y = "Persons",
    title = "COVID-19 fitted vs observed cumulative incidence, Belgium"
  ) +
  scale_colour_manual(
    name = "",
    values = c(red = "red", black = "black", green = "green", blue = "blue"),
    labels = c("Susceptible", "Observed", "Recovered", "Infectious")
  ) +
  theme_minimal()

More summary statistics

Other interesting statistics can be computed from the fit of our model. For example:

the peak of the pandemic
the number of severe cases
the number of people in need of intensive care
the number of deaths

fit <- fitted_cumulative_incidence

# peak of pandemic
fit[fit$I == max(fit$I), c("Date", "I")]

##          Date        I
## 89 2020-05-02 531000.4

# severe cases
max_infected <- max(fit$I)
max_infected * 0.2

## [1] 106200.1

# cases with need for intensive care
max_infected * 0.06

## [1] 31860.03

# deaths with supposed 4.5% fatality rate
max_infected * 0.045

## [1] 23895.02

Given these predictions, with the exact same settings and no intervention at all to limit the spread of the pandemic, the peak in Belgium is expected to be reached by the beginning of May. About 530,000 people would be infected by then, which translates to about 106,000 severe cases, about 32,000 persons in need of intensive care (given that there are about 2000 intensive care units in Belgium, the health sector would be completely overwhelmed) and up to 24,000 deaths (assuming a 4.5% fatality rate, as suggested by this source).

At this point, we understand why such strict containment measures and regulations are taken in Belgium!

Note that those predictions should be taken with a lot of caution. On the one hand, as mentioned above, they are based on rather unrealistic assumptions (for example, no public health interventions, fixed reproduction number $R_0$, etc.). More advanced projections are possible with the {projections} package, among others (see this section for more information on this matter). On the other hand, we still have to be careful and strictly follow public health interventions because previous pandemics such as the Spanish and swine flu have shown that incredibly high numbers are not impossible!

The purpose of this article was to give an illustration of how such analyses are done in R with a simple epidemiological model. Those are the numbers our simple model produces and we hope they are wrong because the cost in terms of lives would be enormous.

Additional considerations

As previously mentioned, the SIR model and the analyses done above are rather simplistic and may not give a true representation of the reality. In the following sections, we highlight five improvements that could be done to enhance theses analyses and lead to a better overview of the spread of the Coronavirus in Belgium.

Ascertainment rates

In the previous analyses and graphs, it is assumed that the number of confirmed cases represent all the cases that are infectious. This is far from reality as only a proportion of all cases are screened, detected and counted in the official figures. This proportion is known as the ascertainment rate.

The ascertainment rate is likely to vary during the course of an outbreak, in particular if testing and screening efforts are increased, or if detections methods are changed. Such changing ascertainment rates can be easily incorporated into the model by using a weighting function for the incidence cases.

In his first article, Tim Churches demonstrates that a fixed ascertainment rates of 20% makes little difference to the modelled outbreak with no intervention, except that it all happens a bit more quickly.

More sophisticated models

More sophisticated models could also be used to better reflect real-life transmission processes. For instance, another classical model in disease outbreak is the SEIR model. This extended model is similar to the SIR model, where S stands for Susceptible and R stands for Recovered, but the infected people are divided into two compartments:

E for the Exposed/infected but asymptomatic
I for the Infected and symptomatic

These models belong to the continuous-time dynamic models that assume fixed transition rates. There are other stochastic models that allow for varying transition rates depending on attributes of individuals, social networking, etc.

Modelling the epidemic trajectory using log-linear models

As noted above, the initial exponential phase of an outbreak, when shown in a log-linear plot (the y-axis on a log scale and the x-axis without transformation), appears (somewhat) linear. This suggests that we can model epidemic growth, and decay, using a simple log-linear model of the form:

\[log(y)=rt+b\]

where y is the incidence, r is the growth rate, t is the number of days since a specific point in time (typically the start of the outbreak), and b is the intercept. In this context, two log-linear models:

one to the growth phase (before the peak), and
one to the decay phase (after the peak)

are fitted to the epidemic (incidence cases) curve.

The doubling and halving time estimates which you very often hear in the news can be estimated from these log-linear models. Furthermore, these log-linear models can also be used on the epidemic trajectory to estimate the reproduction number $R_0$ in the growth and decay phases of the epidemic.

The {incidence} package in R, part of the R Epidemics Consortium (RECON) suite of packages for epidemic modelling and control, makes the fitting of this kind of models very convenient.

Estimating changes in the effective reproduction number $R_e$

In our model, we set a reproduction number $R_0$ and kept it constant. It would nonetheless be useful to estimate the current effective reproduction number $R_e$ on a day-by-day basis so as to track the effectiveness of public health interventions, and possibly predict when an incidence curve will start to decrease.

The {EpiEstim} package in R can be used to estimate $R_e$ and allow to take into consideration human travel from other geographical regions in addition to local transmission (Cori et al. 2013; Thompson et al. 2019).

More sophisticated projections

In addition to naïve predictions based on a simple SIR model, more advanced and complex projections are also possible, notably, with the {projections} package. This packages uses data on daily incidence, the serial interval and the reproduction number to simulate plausible epidemic trajectories and project future incidence.

Conclusion

This article started with (i) a description of a couple of R resources on the Coronavirus pandemic (i.e., a collection and a dashboard) that can be used as background materials and (ii) the motivations behind this article. We then detailed the most common epidemiological model, i.e. the SIR model, before actually applying it on Belgium incidence data.

This resulted in a visual comparison of the fitted and observed cumulative incidence in Belgium. It showed that the COVID-19 pandemic is clearly in an exponential phase in Belgium in terms of number of confirmed cases.

We then explained what is the reproduction number and how to compute it in R. Finally, our model was used to analyze the outbreak of the Coronavirus if there was no public health intervention at all.

Under this (probably too) simplistic scenario, the peak of the COVID-19 in Belgium is expected to be reached by the beginning of May, 2020, with around 530,000 infected people and about 24,000 deaths. These very alarmist naïve predictions highlight the importance of restrictive public health actions taken by governments, and the urgency for citizens to follow these health actions in order to mitigate the spread of the virus in Belgium (or at least slow it enough to allow health care systems to cope with it).

We concluded this article by describing five improvements that could be implemented to further analyze the disease outbreak.

Note that this article has been subject to a talk at UCLouvain.

Thanks for reading. I hope this article gave you a good understanding of the spread of the COVID-19 Coronavirus in Belgium. Feel free to use this article as a starting point for analyzing the outbreak of this disease in your own country.

For the interested readers, see also:

the evolution of hospital admissions and number of confirmed cases in Belgium
a collection of top R resources on Coronavirus to gain even further knowledge

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

References

Fine, Paul, Ken Eames, and David L Heymann. 2011. “"Herd Immunity": A Rough Guide.” Clinical Infectious Diseases 52 (7): 911–16.

Thompson, RN, JE Stockwin, RD van Gaalen, JA Polonsky, ZN Kamvar, PA Demarsh, E Dahlqwist, et al. 2019. “Improved Inference of Time-Varying Reproduction Numbers During Infectious Disease Outbreaks.” Epidemics 29: 100356.

Feel free to let me know in the comments or by contacting me if you performed some analyses specifically for Belgium and which I could include in my article covering the top R resources on the Coronavirus.↩︎
See a more detailed note on the reproduction number by James Holland Jones if you need a deeper understanding.↩︎

How to create a simple Coronavirus dashboard specific to your country in R?

Mon, 23 Mar 2020 00:00:00 +0000

Coronavirus dashboard: the case of Belgium

Introduction

The Novel COVID-19 Coronavirus is the hottest topic right now. Every day, the media and newspapers share the number of new cases and deaths in several countries, try to measure the impacts of the virus on citizens and remind us to stay home in order to stay safe. The Coronavirus is on everyone’s lips.

In addition to governments, media and companies discussing about it, data scientists and data professionals in general are putting their knowledge and time at the service of the virus. This leads to a proliferation of applications, dashboards, blog posts, videos, datasets and code analyzing, in one way or another, the expansion of the COVID-19 and how it spreads in the population.

Top R resources on Coronavirus

As a data lover myself, I discovered a multitude of great resources about the Coronavirus. However, these resources were spread all over the internet and were often hidden by the mass of information of another type (e.g., alarming headlines, names of infected celebrities, companies showing off how they helped health care agencies, etc.). To tackle this issue, I collected and then shared in a previous article the best R resources on the Coronavirus I came across.

Note that only resources on R are shared in this article as R is my favorite statistical program and the one I am most familiar with. The fact that I use this program almost daily makes it easier for me to realize the complexity and time put behind a resource, and appreciate its quality and its potential.

I am sure that there are other very interesting resources online (see for example the probably most popular dashboard by the Johns Hopkins Coronavirus Resource Center).¹ Nonetheless, a lot of people are in a better position than I am to judge the quality of resources made with programming languages for which I am not competent.

This article made me discover so many great resources about the Coronavirus and I keep receiving data visualizations and data analyses from scientists all over the world so that I include them in the collection. Thanks for that, it continuously improves the quality and completeness of the collection.

In addition to receiving R resources, a question often asked by readers was “How can I create a dashboard myself?” or “How can I build a dashboard specific to my country?”. I therefore thought it would serve some people if I created a dashboard specific to my country (Belgium) and detailed the steps on how to build it.

Questions on how to develop such dashboards came mostly from R beginners as advanced R users most probably know how to do one, or at least can easily use the resources I collected here as sources of inspiration for their own work. Furthermore, in response to the craze about the Coronavirus, interested users were quite in a hurry and wanted to have their own dashboard running as quickly as possible.

These questions led me to the idea of creating a simple (yet powerful and visually appealing) dashboard, as opposed to a Shiny app. Shiny apps have the advantage that they are interactive in the sense that users can edit the outputs and the visualizations by simply changing some inputs in a user-friendly way, while dashboards are static and cannot be modified by the final user. On the other hand, the advantage of a dashboard over a Shiny app is that it is much easier to code, especially if you are already proficient in R Markdown.

Coronavirus dashboard: the case of Belgium

Amongst all the visualizations I have seen so far, one is standing out by its simplicity and at the same time, by its completeness and by the quality of its visualizations. I thus decided to create a Coronavirus dashboard based on this already existing dashboard by Rami Krispin (which comes with a license that allows to be freely adapted and shared) and adapt it so that it is specific to Belgium. Note that I also removed some visualizations and tables from the initial dashboard to keep it really simple and straight to the point.

Before reading further, here is my Coronavirus dashboard adapted to Belgium and previews of the main sections below:

The dashboard is segmented into several sections that can be selected at the top:

The summary section provides key measures about the Coronavirus (total cases, active cases and deaths) and a plot displaying the cumulative number of active cases and deaths from January 22, 2020 to the latest available date.
The comparison section presents a comparison of the number of daily new cases (left panel) and the distribution of cases by type (right panel) with other European countries (you can also change these countries by replacing them in the code).
The map section shows a world map of the confirmed cases and deaths. You can uncheck one or several types of cases (top right corner) and zoom in or out (top left corner) to adapt the map to your needs.
The about section gives more information about the data, the dashboard in general and how often it is updated.

I believe this simple dashboard is easy enough to be adapted to any country (and by anyone from beginner to expert), and still communicate key measures about the virus throughout some visualizations.

A little extra which is worth mentioning is the fact that all plots are generated with the {plotly} package. This package allows to enhance plots by displaying additional relevant information when hovering over them (try by yourself!).

How to create your own Coronavirus dashboard

If you want to build your own dashboard specific to a country, follow these steps:

Open the dashboard here
See the entire code via the button “Source code” located in the top right corner of the dashboard, or see the code on GitHub. Copy that code.
Open a new R Markdown file (.Rmd), type any title and author (they will be replaced in the next step anyway), select HTML as the output format and click on OK:

Remove all the template code already present and paste the code you copied in step 1.
Make sure that the required packages are installed:

install.packages(c("devtools", "flexdashboard", "leaflet", "leafpop"))
devtools::install_github("RamiKrispin/coronavirus", force = TRUE)

If R asks you which package you would like to update, there should be no need to update them: type 3 for “None”.

In the code, replace Belgium with your country. Here is the list of all available countries in the dataset:

Canada, United Kingdom, China, Netherlands, Australia, New Zealand, Denmark, France, Afghanistan, Albania, Algeria, Andorra, Angola, Antarctica, Antigua and Barbuda, Argentina, Armenia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Costa Rica, Cote d’Ivoire, Croatia, Cuba, Cyprus, Czechia, Diamond Princess, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Holy See, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea, North, Korea, South, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, MS Zaandam, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Summer Olympics 2020, Suriname, Sweden, Switzerland, Syria, _Taiwan*_, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, United Arab Emirates, Uruguay, US, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Winter Olympics 2022, Yemen, Zambia and Zimbabwe

Note that if your country is spelled in two words or more, you will need to surround it by a backtick (but only at one specific line in the code, see an example with United Kingdom):

#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~`United Kingdom`,
    type = "scatter",
    mode = "lines+markers",
    name = "United Kingdom"
  ) %>%

Do not add backticks in the rest of the code as everywhere else the country’s name is surrounded by double quotes "".

Do not forget to also change the title and the author at the top of the document, and edit the about section at the bottom of the document. Last but not least, as you can see on the plot in the summary section, the arrows point to different (sad) “milestones” in Belgium (i.e., first case, first death and new containment measures).

You will need to adapt these milestones for your country (or remove them if you do not want to have any milestone displayed on the plot). Change this in the code following the plotly::add_annotations() functions.

Knit the document (see this article if you are unfamiliar with R Markdown). Your dashboard should appear in HTML format.

Following these 7 steps, you should already have a simple dashboard specific to your country. I have intentionally kept it simple so that everyone could copy it and have their own dashboard in a limited amount of time.

If you are familiar with the {flexdashboard}, {plotly} and {leaflet} packages for the dashboard interface and the visualizations, and the {dplyr} and {tidyr} packages for the data manipulation, feel free to edit the code according to your needs and improve your dashboard.

Additional notes

Data

The input data for this dashboard is the dataset available from the {coronavirus} R package. Make sure to download the development version of the package to have the latest data:

install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")

To update your dashboard with the latest data, you have to manually update the data by reinstalling the {coronavirus} package with devtools::install_github("RamiKrispin/coronavirus", force = TRUE). Again, if R asks you whether you would like to update other packages, type 3 for “None”.

This question was often raised so I repeat, your dashboard will not update by itself every day, you need to manually update it. After updating the data, you may also need to restart your R session in order to have the last available data.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

Open source

This dashboard and the code available on GitHub are open source so feel free to copy it, adapt it and share it as much as you want.

Accuracy

Please note that this dashboard has been built mainly for educational purposes. I update the dashboard as often as possible to keep it accurate. However, there is some uncertainty concerning the COVID-19 case numbers and the testing methods vary between countries so the figures on this dashboard may be slightly different compared to other sources. Currently, the maintainer of the dataset updates it on a daily basis, but updates may become less frequent in the future.

Publish your dashboard

If you want to share your dashboard, you can either:

Upload it on your website if you have one (and I strongly suggest you to create one if you do not already have one)
Publish it through RPubs (it is free and easy to publish your work directly from RStudio)

Conclusion

Thanks for reading.

I hope this article helped you to build your first Coronavirus dashboard in R. See these top R resources on Coronavirus if you need inspiration to enhance further your dashboard.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

I would be glad to mention a collection of Python resources if someone is willing to create a collection of resources about the Coronavirus made with this programming language. Feel free to contact me if this is the case.↩︎

Top 100 R resources on COVID-19 Coronavirus

Thu, 12 Mar 2020 00:00:00 +0000

Warning: Some links or resources may have been moved or deleted, and are thus not accessible anymore. If you are the author and would like to update the URL, feel free to contact me so I can update the link.

The Coronavirus is a serious concern around the globe. With its expansion, there are also more and more online resources about it. This article presents a selection of the best R resources on the COVID-19 virus.

This list is by no means exhaustive. I am not aware of all R resources available online about the Coronavirus, so please feel free to let me know in the comments or by contacting me if you believe that another resource (R package, Shiny app, R code, blog posts, datasets, etc.) deserves to be on this list.

R Shiny apps and dashboards

Coronavirus tracker

Developed by John Coene, this Shiny app tracks the spread of the Coronavirus, based on three data sources (John Hopkins, Weixin and DXY Data). The Shiny app, built with shinyMobile (which makes it responsive on different screen sizes), presents in a really nice way the number of deaths, confirmed, suspected and recovered cases by time and region.

The code is available on GitHub.

Coronavirus dashboard from the `{coronavirus} package`

Developed by the author of the {coronavirus} package, this dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The data and dashboard are refreshed on a daily basis.

The code is available on GitHub.

From this dashboard, I created another dashboard specific to Belgium. Feel free to use the code available on GitHub to build one specific to your country. See more details in this article.

Visualization of Covid-19 Cases

Developed by Nico Hahn, this Shiny app uses leaflet, plotly and the data from Johns Hopkins University to visualize the outbreak of the novel Coronavirus and shows data for the entire world or singular countries.

The code is available on GitHub.

Modeling COVID-19 Spread vs Healthcare Capacity

Developed by Dr. Alison Hill, this Shiny app uses an epidemiological model based on the classic SEIR model to describe the spread and clinical progression of COVID-19. It includes different clinical trajectories of infection, interventions to reduce transmission, and comparisons to healthcare capacity.

The code is available on GitHub.

COVID-19 Data Visualization Platform

Developed by Shubhram Pandey, this Shiny app provides a clear visualization of Covid19 impact all over the world and it also provides a sentiment analysis using natural language processing from Twitter.

The code is available on GitHub.

Coronavirus 10-day forecast

Developed by the Spatial Ecology and Evolution Lab, this Shiny app gives a ten-day forecast, by country, on likely numbers of Coronavirus cases and gives citizens a sense of how fast this epidemic is progressing.

See a detailed explanation of the app and how to read it in this blog post. The code is available on GitHub.

Coronavirus (COVID-19) across the world

Developed by Anisa Dhana in collaboration with datascience+, this Shiny app monitors the spread of COVID-19 across the world via a map visualization of the confirmed cases and some graphs on the growth of the virus.

The dataset used is from Johns Hopkins CSSE and part of the code is available in this blog post.

COVID-19 outbreak

Developed by Dr. Thibaut Fabacher in collaboration with the department of Public Health of the Strasbourg University Hospital and the Laboratory of Biostatistics and Medical Informatics of the Strasbourg Medicine Faculty, this Shiny app shows an interactive map for global monitoring of the infection. It focuses on the evolution of the number of cases per country and for a given period in terms of incidence and prevalence.

The code is available on GitHub and this blog post discusses it in more detail.

Flatten the Curve

Developed by Tinu Schneider, this Shiny app illustrates, in an interactive way, the different scenarios behind the #FlattenTheCurve message.

The app has been built upon Michael Höhle’s article and the code is available on GitHub.

Explore the spread of Covid-19

Developed by Joachim Gassen, this Shiny app allows you to visualize confirmed, recovered cases and reported deaths for several countries via one summary graph.

The Shiny app is based on data from:

Johns Hopkins University CSSE team on the spread of the SARS-CoV-2 virus
ACAPS governmental measures database
World Bank

This blog post explains the Shiny app in further details and in particular the {tidycovid19} R package behind it.

Governments and COVID-19

Developed by Sebastian Engel-Wolf, this Shiny app presents in a elegant way the following measurements:

Maximum time of exponential growth in a row
Days to double infections
Exponential growth today
Confirmed cases
Deaths
Population
Confirmed cases on 100,000 inhabitants
Mortality rate

The code is available on GitHub and this article explains it in further details.

Simulating COVID-19 Epidemic in Togo - West Africa

Developed by Dr. Kankoé Sallah, this Shiny app uses SEIR metapopulation model with mobility between catchment areas to describe country level spread of COVID-19 and the impact of interventions in Togo, West Africa.

Covid-19 Prediction

Developed by Manuel Oviedo and Manuel Febrero (Modestya research group of the University of Santiago de Compostela), this Shiny app predicts the growth rate at 5-day horizon using the evolution during the last 15 days of growth rate. Three functional regression models are fitted and re-estimated when new data is available. The app also shows an interactive plot and table for the expected number of accumulated cases and new daily cases to each horizon (for confirmed and deaths responses) by country (from Johns Hopkins CSSE) and Spanish region (from ISCII).

See an explanation of the methodology in the About tab.

Covid-19 Dashboard

Developed by Philippe De Brouwer, this dashboard displays several key measures regarding the outbreak of the virus (by country or for all countries combined), together with some forecasts, a world map and other interactive plots.

Healthcare worker deaths from novel Coronavirus (COVID-19) in the US

Developed by Jonathan Gross, this Shiny app visualizes healthcare worker deaths from Coronavirus (COVID-19) in the US reported in the news. It is updated daily and the code is available on GitHub.

Covid-19 Hospitalizations in Belgium

Developed by Jean-Michel Bodart, this dashboard provides an overview of the evolution of Covid-19-related hospitalizations in Belgium, by region and province.

The code is available on GitHub.

COVIDMINDER: Where you live matters!

Developed by The Rensselaer Institute for Data Exploration and Applications, this Shiny app reveals the regional disparities in outcomes, determinants and medications (e.g., mortality rates, test cases, diabetes, and hospital beds) across United States, with a special focus on New York.

This blog post explains the Shiny app in more detail and the code is available on GitHub.

COVID-19 Canada Data Explorer Tool

Developed by Petr Baranovskiy from Data Enthusiast’s Blog, this Shiny app processes the official dataset available from the Government of Canada and shows several indicators related to the SARS-CoV-2 epidemic in Canada.

This blog post details the application in further detail.

Philippine COVID-19 Case Forecasting

Developed by Jamal Kay Rogers and Yvonne Grace Arandela, this Shiny app provides a 5-day forecast of confirmed positive, deaths, and recoveries of COVID-19 cases in The Philippines.

The app also delivers graphical plots of a 10-day forecast and the daily and cumulated cases of COVID-19 in The Philippines. The data source is Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

COVID-19 Case & Death Report Number Corrector

Developed by Matt Maciejewski, this Shiny app focuses on the correction of underreported Covid-19 case and death counts using a reference country based on Lachmann et al. (2020), and via a multiplicative estimator for total deaths and cases. The estimator will be turned into a posterior prediction once data becomes available.

The app is explained in more detail in this article and the code of the Shiny app can be found on GitHub.

Covid-19: the SPQEIR model

Developed by several researchers from the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg, the KU Leuven and the UGent, this Shiny app uses the new SPQEIR model to simulate the impact of various suppression strategies (social distancing, lockdown, protection, etc.) on the development of COVID-19.

The Belgian Covid Cases Tracker

Developed by Patrick Sciortino, this Shiny app aims at estimating the curve of true Covid-19 cases based on the idea that a hospitalized case in time t informs us about an infection that took place a few days earlier.

COVID-19 monitor

Developed by Trafford Data Lab, this Shiny app visualizes daily confirmed Coronavirus cases and deaths in the UK.

It uses the following data sources:

The code can be found on GitHub.

COVID-19 Bulletin Board

Developed by Wei Su, this dashboard shows real-time visualization of the COVID-19 epidemic in Japan. It mainly shows various indicators including, but not limited to, PCR test, positive confirmed, hospital discharge and death, as well as trends in each prefecture in Japan. There are also a variety of charts such as cluster network, new confirmed cases in log scale for users’ reference.

The dashboard is based on this Japanese version (developed by the same author). The code can be found on GitHub.

Covid-19 Statistics Displayer

Created by Carl Sansfaçon, this Wordpress plugin associates R {ggplot2} graphics with ARIMA forecast and PHP coding to show evolution of the confirmed, death and recovered cases in different countries, states/provinces and US cities.

It uses data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The plugin can be installed as a Wordpress plugin.

CoronaMapper

Supported by OxyLabs, created by Paolo Montemurro and Peter, this visualization displays the four days average growth indicator, which clearly shows how a certain statistic of the virus is evolving over time, filtering out the noise.

The website receives data each hour from several official data-sources, and visualizes the historical evolution of COVID19 in an intuitive & interactive way.

CoronaDash

Developed by Peter Laurinec, this Shiny app provides various data mining and visualization techniques for comparing countries’ COVID-19 data statistics as:

extrapolating total confirmed cases by exponential smoothing model,
trajectories of cases/deaths spread,
multidimensional clustering of countries’ data/ statistics - with dendrogram and table of clusters averages,
aggregated views for the whole world,
hierarchical clustering of countries’ trajectories based on DTW distance and preprocessing by SMA (+ normalization), for fast comparison of a large number of countries’ COVID-19 magnitudes and trends.

This blog post explained in further detail the last point of the above list. The code of the app is available on GitHub.

Covidfrance

Developed by Guillaume Pressiat, this Shiny app illustrates the evolution of number of hospitalizations, intensive care units, recoveries and deaths in France (by department).

This blog post presents the application and the code of the app is available on GitHub.

The Impact of COVID-19 on Mobility

Developed by Dimiter Toshkov, this Shiny app shows relative changes in mobility (visits and length of stay) for a specific category of places within a country compared to a baseline. The baseline is computed as the median for the day of the week during the 5-week period between January 3 and February 6, 2020. Hence, the plot shows how mobility has changed relative to the situation in the same country in the beginning of the year.

The author also developed a version for the US states.

Data is from Google Community Mobility Reports.

Coronavirus Analysis Platform

Developed by Khaled M Alqahtani, this Shiny app offers a powerful analysis tools, including:

Descriptive analysis
Growth rate and curve flatting
Cumulative forecast
Daily cases forecasting containing 16 different models
Newspaper analysis
TV analysis

Data is from Johns Hopkins CSSE and some GDELT APIs.

COVID-19 Tracker

Developed by Dr. Magda Bucholc from Ulster University, this dashboard reports cases at the local government district in Northern Ireland and county level across the island of Ireland, providing gender and age breakdowns of reported cases, growth rates, and statistics per 100,000 of the population; it also has daily mobility data from Google and Apple.

More information about this dashboard can be found here.

COVID-19 Overview

Developed by Fabian Dablander, Alexandra Rusu, Marcel Schreiner, and Aleksandar Tomasevic as part of the Science versus Corona project, this dashboard provides an overview of confirmed cases, deaths, and measures that countries have taken to curb the spread of the virus.

For a more information about this dashboard, see this blog post.

COVID-19 Exit Strategies

Developed as part of the Science versus Corona project, this Shiny app compares several alternative exit strategies that either aim to keep the number of infections as low as possible (e.g., contact tracing), or that aim to develop herd immunity without exceeding health care capacity.

The Shiny app is based on the stochastic individual-based SEIR model developed by de Vlas and Coffeng (2020). This post explains the Shiny app in more detail.

Corona Virus Statistics : COVID19

Developed by Dr. Mohammed N. Alenezi, this dashboard displays the latest information about Coronavirus for Kuwait, GCC, and the world. The dashboard contains a collection of different plots and models presented under 8 tabs.

Covid19 Data

Developed by Dhafer Malouche, this dashboard presents statistics for more than 200 countries and regions, with the estimation of the reproduction number $R(t)$ in the previous 60 days and a Covid19 country classification.

WHO COVID-19 Explorer

Developed by the World Health Organization (WHO), this Shiny app aims to provide frequently updated data visualizations about confirmed cases and deaths at the global, regional and country level.

COVID-19 Scenario Analysis Tool

Developed by the MRC Centre for Global Infectious Disease Analysis (Imperial College London), this dashboard illustrates the epidemic trajectory, the healthcare demand and the $R_t$ & $R_{eff}$ measures for many countries over time in interactive plots.

The dashboard uses the squire R package, among others.

Covid-19 track

Developed by Redzuan, this Shiny dashboard analyses, combines and harmonizes the latest Covid-19 data into progress timeline, comparative analysis on various areas, mapping, latest news, forecasting using various models, prediction on Herd Immunity target, summary table, downloadable fact sheet and other features.

R packages

`{nCov2019}`

The {nCov2019} package gives you access to epidemiological data on the Coronavirus outbreak.¹ The package gives real-time statistics, includes historical data and a Shiny app. The vignette explains the main functions and possibilities of the package.

Furthermore, the authors of the package also developed a website with interactive plots and time-series forecasts, which could be useful in informing the public and studying how the virus spread in populous countries.

`{coronavirus}`

Developed by Rami Krispin, the {coronavirus} package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. Pulled from the dataset of John Hopkins, the R package gives a daily summary of the Coronavirus cases by state/province. The data set contains various variables such as confirmed cases, death, and recovered across different countries and states.

More details are available here, a csv format of the package dataset is available here and a summary dashboard is available here.

`{tidycovid19}`

Developed by Joachim Gassen, the {tidycovid19} package allows you to download, tidy and visualize Covid-19 related data (including data on governmental measures) directly from authoritative sources. It also provides a flexible function and an accompanying Shiny app to visualize the spreading of the virus.

The package is available on GitHub and these blog posts here and here explain it in more detail.

R packages from R Epidemics Consortium

These R packages from R Epidemics Consortium allows you to find the most advanced tools used by professional epidemiologists and experts in the domain of analyzing disease outbreaks.

`{covdata}`

Published by Prof. Kieran Healy, the {covdata} package is a R package providing COVID-19 case data from multiple sources:

National level data from the European Centers for Disease Control
State-level data for the United States from the COVID Tracking Project
State-level and county-level data for the United States from the New York Times
Data from the US Centers for Disease Control’s Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)
Data from Apple on relative trends in mobility in cities and countries since mid-January of 2020, based on usage of their Maps application
Data from Google on relative trends in mobility in regions and countries since mid-January of 2020, based on location and activity information

The code is available on GitHub.

`{covid19italy}`

The covid19italy R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Italy. The package includes the following three datasets:

italy_total: daily summary of the outbreak on the national level
italy_region: daily summary of the outbreak on the region level
italy_province: daily summary of the outbreak on the province level

More information about the package datasets available in this vignette, this blog post, and this supporting dashboard.

Data source: Italy Department of Civil Protection

`{COVID19}`

The goal of COVID-19 Data Hub is to provide the research community with a unified data hub by collecting worldwide fine-grained case data, merged with exogenous variables helpful for a better understanding of COVID-19. Featured by the University of Milano and funded by the Institute for Data Valorization IVADO, Canada.

The package collects COVID-19 data across governmental sources, includes policy measures from Oxford COVID-19 Government Response Tracker, and extends the dataset via an interface to World Bank Open Data, Google Mobility Reports and Apple Mobility Reports.

The package is available on CRAN, it is 100% open source and external contributors are welcomed to join.

`{COVOID}`

COVOID (for COVID-19 Open-source Infection Dynamics project) is a R package for modelling COVID-19 and other infectious diseases using deterministic compartmental models (DCMs).

It contains a built-in Shiny app enabling easy use and demonstration of key concepts to those without R programming backgrounds, along with an expanding API for simulating and estimating homogeneous and age-structured SIR, SEIR and extended models. In particular COVOID allows the simultaneous simulation of age specific (e.g. school closures) and general interventions over varying time intervals.

The code is available on GitHub.

`{cdccovidview}`

Published by Bob Rudis, the {cdccovidview} package can be used to work with the U.S. CDC’s New COVID-19 Trackers: COVIDView and COVID-NET.

`{babsim.hospital}`

Published by several researchers from TH Köln, the {babsim.hospital} package implements a discrete-event simulation model for a hospital resource planning problem. It can be used by health departments to forecast demand for intensive care beds, ventilators, and staff resources.

The team also developed a Shiny app which predicts COVID-19 ICU bed resources in hospitals. The app is available in English and German.

`{EpiLPS}`

Published by several researchers working in the field of epidemiology, the package shows how to smooth epidemic curves and estimate the time-varying reproduction number in a flexible way.

More information can be found in:

the research paper,
the accompanying website, and
this summary written by one of the authors.

R code and blog posts

Analyzing COVID-19 outbreak data with R

Written by Tim Churches, these two articles (part 1 and part 2) explore the R tools and packages that might be used to analyze the COVID-19 data. In particular, the author considers when the pandemic will subside in China, and then turns the analysis on Japan, South Korea, Italy and Iran. He also shows improvements on the cumulative incidence plots that are so common. Moreover, he presents R code to analyze how contagious is the Coronavirus thanks to the classic SIR (Susceptible-Infectious-Recovered) compartmental model of communicable disease outbreaks.²

The code is available on GitHub (part 1 and part 2).

Part 1 is actually based on another shorter blog post by Prof. Dr. Holger K. von Jouanne-Diedrich from Learning Machines. Read his article for a more concise analysis on how to model the outbreak of the Coronavirus and discover how contagious it is. Note that I have personally written an article analyzing COVID-19 in Belgium based on articles from these two authors.

More recently, Tim Churches published a series of other interesting articles:

Modelling the effect of various public health interventions on the local epidemic spread of COVID-19 infection using stochastic individual compartmental models (ICMs) implemented by the {EpiModel} package for R:
- Part 1 (code here)
- Part 2 (code here)
The use of simulations to explore the effects of various interventions on COVID-19 spread
COVID-19 epidemiology with R: in this blog post, the author, using relatively early and partial US data, separates out inbound from community cases, and predicts the next few weeks of incident numbers. He also highlights several R functions to analyze a disease outbreak.
We can “shrink” the COVID-19 curve, rather than just flatten it (in collaboration with Louisa Jorm)

COVID-19 Data Analysis with `{tidyverse}` and `{ggplot2}`

Dr. Yanchang Zhao from RDataMining published a data analysis around the Coronavirus with the {tidyverse} and {ggplot2} packages, for China and world wide.

Both documents are a mix of data cleaning, data processing and visualizations of the confirmed/cured cases and death rates across countries or regions.

COVID-19 cumulative observed case fatality rate over time

Written by Peter Ellis, this article focuses on how the observed case fatality rate of COVID-19 has evolved over time across 7 countries and comments on why the rates vary (low testing rates, age of the population, overwhelmed hospitals, etc.).

The code is available at the end of the article. The data is from John Hopkins and it uses the {coronavirus} package.

More recently, the author published a series of other articles:

Impact of a country’s age breakdown on COVID-19 case fatality rate: it looks at estimated fatalities in different countries according to the age distributions in those countries (based on Italy’s data). The data is from The Istituto Superiore di Sanità (Roma) and all the code is shown in the post.
How to make that crazy Fox News y axis chart with ggplot2 and scales: less about COVID19 than about how a bizarre Fox News graph can be re-created with the correct transformations needed to make its scale appropriate.
Test positivity rates and actual incidence and growth of diseases: this blog post looks at several different ways of accounting for the information given to us by high positive testing rates for COVID-19 and looks at the impact on estimates of effective reproduction number at a point in time.
Incidence of COVID-19 in Texas after adjusting for test positivity: the author examines the trends in COVID-19 cases in Texas, with and without being adjusted by a multiplier of the square root of the test positivity rate.

Covid 19 Tracking

Written by Prof. Kieran Healy, this article discusses how to get an overview of best-available counts of deaths, using the COVID-19 Data from the European Centers for Disease Control.

Code can be found in the article and on GitHub.

More recently, the author published three other articles:

A COVID Small Multiple: this article discusses how to create a small-multiple plot of cases by country, showing the trajectory of the outbreak for a large number of countries, with the background of each small-multiple panel also showing (in grey) the trajectory of every other country for comparison.

Apple’s COVID Mobility Data: this article uses Apple’s time series mobility data for several cities and countries (via the {covdata} package) to plot three modes of getting around: driving, public transit, and walking. The series begins on January 13th indexed to 100 at the beginning of the series, so trends are relative to that baseline.

New Orleans and Normalization: this article responds to a thoughtful post by Dr. Drang regarding an improvement in normalization of the data.

Infectious diseases and nonlinear differential equations

Published by Fabian Dablander, this math intensive blog post explains what SIR and SIRS models take into account and how they calculate their results.

From a pandemic perspective, the author writes “The SIRS model extends the SIR model, allowing the recovered population to become susceptible again (hence the extra ‘S’). It assumes that the susceptible population increases proportional to the recovered population”.

More recently, the author, in collaboration with other researchers, published another blog post outlining a number of excellent visualizations of the COVID19 pandemic, as well as presenting their own dashboard.

Epidemic modelling of COVID-19 in the UK using an SIR model

Published by Thomas Wilding, this blog post applies the SIR model to UK data.

As further extensions to the model, the author suggests:

Using an SEIR model (adding an Exposed compartment for people who are infected but not yet infectious)
Adding a “Q” layer since a lot of people are being Quarantined or isolated
Considering the “hidden”” population that is infected but is denied being tested due to shortage of tests
Feasibility of a second wave / outbreak of the epidemic later in the year (as seen in previous outbreaks, such as Swine Flu)

Data sources:

Modeling Pandemics

Published by Arthur Charpentier, this series of 3 blog post (part 1, part 2, part 3) walks through the SIR model and its parameters, how ODEquations solves it, and generating the reproductive rate. It also gives a mathematical explanation of a model for how quickly a pandemic will return, albeit with diminishing intensity. Last, it explains a model that is more sophisticated than SIR, the SEIR model, and illustrates it with Ebola data.

More recently, the author published another article which examines what proportion of the population in various U.S. states have been tested for the novel Coronavirus and tries to answer the following two questions:

How many people are tested on a daily basis?
What are we actually testing for?

Last but not least, this post reproduces one of his scientific paper entitled “COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability”.

COVID-19: The Case of Germany

Published by Prof. Dr. Holger K. von Jouanne-Diedrich from Learning Machines, this blog post uses the SIR model and German data to estimate the duration and severity of the pandemic.

Download the data from Morgenpost.

More recently, the author published other articles:

COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths: Working back from reported deaths from Covid19, the post shows how to estimate infections at a prior date, based on several assumptions about fatality rates and infected periods (and acknowledging many unknowns and data problems).
How to analyze mobility trends with R using anonymized and aggregated Apple’s mobility data available to the public. The article presents a R function to return the data in a well-structured format for countries and major cities, and to visualize the drop in vehicular and pedestrian movement caused by the pandemic.

COVID-19: False Positive Alarm, demonstrating the importance of infection rates on the likelihood that someone testing positive for the Coronavirus is actually positive.

Flatten the COVID-19 Curve

Published by Michael Höhle from Theory meets practice, this blog post discusses why the message of flattening the COVID-19 curve is right, but why some of the visualizations used to show the effect are wrong: Reducing the basic reproduction number does not just stretch the outbreak, it also reduces the final size of the outbreak.

From a pandemic point of view, the author writes “Because of limited health capacities, stretching out the outbreak over a longer time period will ensure, that a larger proportion of those in need of hospital treatment will actually get it. Other advantages of this approach are to win time in order to find better treatment forms and, possibly, to eventually develop a vaccine”.

A Shiny app has also been built upon this article to investigate different scenarios.

In a second article entitled “Effective reproduction number estimation”, Michael Höhle estimates with the {R0} package the time-varying effective reproduction number during an infectious disease outbreak such as COVID-19. Using a single simulated outbreak he compares the performance of three different estimation methods.

More recently, in this article the author looks at “superspreading” in infectious disease transmission from a statistical point of view. He characterises heterogeneity in the offspring distribution [who becomes infected by the superspreader person] by the Gini coefficient instead of the usual dispersion parameter of the negative binomial distribution. This allows us to consider more flexible offspring distributions.

Flattening vs shrinking: the math of #FlattenTheCurve

Published by Ben Bolker and Jonathan Dushoff, this blog post gives a clear explanation of physical distancing and explains how physical distancing makes several beneficial outcomes possible.

The code is available on GitHub.

explainCovid19 challenge

Published by Przemyslaw Biecek, this blog post gives an overview of a model that uses gradient boosting to predict survival based on age, country, and gender. It also shows how older people are more at risk and it lets you play with the model yourself with a modelStudio interactive dashboard.

Data sources:

Google sheet (with most recent data at the end of February)
Kaggle dataset

An R Package to explore the Novel Coronavirus

Published by Patrick Tung via Towards Data Science, this blog post translates into English an R package originally written in Chinese.

Data is collected from Tencent, at https://news.qq.com/zt2020/page/feiyan.htm, which contains one of the most up-to-date public information of the Coronavirus.

Coronavirus model using R – Colombia

Published by Daniel Pena Chavez, this blog post uses the code from Prof. Dr. Holger K. von Jouanne-Diedrich to model height of pandemic in Colombia and projected deaths. The author also points out that a huge number of other variables need to be considered, such as density, climate and government response.

Data is from Rami Krispin’s GitHub.

More recently, the author published another article comparing China and Italy’s rates on log scales.

COVID-19: The Case of Spain

Written by Jose from Diarium - Statistics and R software, this blog post, using data for Spain, applies the SIR model, and then a cubic polynomial regression model to predict infections, hospitalizations, deaths and peak date.

Tidying the new Johns Hopkins Covid-19 time-series datasets

Written by Joachim Gassen, this blog post provides functions and code to deal with different country names and changes on the Johns Hopkins site.

More recently, the author published a series of other interesting articles:

Merge Covid-19 Data with Governmental Interventions Data: this article analyzes five kinds of intervention on the spread of COVID-19.

Scraping Google Covid-19 community movement data from PDF figures: this article explains how to scrape data from a Google site that tracks movements of people. The author uses the {tidycovid19} R package and prepares an analysis of Germany and then across countries.

Covid-19: Explore Your Visualizer Degrees of Freedom: in this article, the author uses COVID-19 data to demonstrate how graphs can communicate very differently, and be manipulated. He shows that getting a ‘neutral’ message to the reader is far from trivial and that visualizations without guidance can be particularly misleading.
{tidycovid19} New data and documentation: A recent update to the {tidycovid19} package brings data on testing, alternative case data, some regional data and proper data documentation. Using all this, you can use the package to explore the associations of (the lifting of) governmental measures, citizen behavior and the Covid-19 spread.
Exploring and Benchmarking Oxford Government Response Data: A post assessing the impact of non-pharmaceutical interventions on the spread of Covid-19 based on the Assessment Capacities Project (ACAPS) and the Oxford Covid-19 Government Response Tracker.

COVID-19 in Belgium

Based on Tim Churches’ article, I published an analysis of the COVID-19 specifically for Belgium. In this article, I also use the most common epidemiological model, the SIR model (to its simplest form), to analyze the outbreak of the disease in the case where there would be no public health intervention. I also show how to compute the reproduction number and I present some additional improvements that can be made to further analyze the epidemic.

The code is available on GitHub, so feel free to use it as starting point for an analysis of the virus outbreak in your own country.

Facts About Coronavirus Disease 2019 (COVID-19) in 5 Charts created with R and ggplot2

Written by Gregory Kanevsky, this blog post compiles some useful facts about COVID-19 into 5 charts, including gauge charts, and discusses R and {ggplot2} techniques used to create them.

Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting

Written by Martijn Weterings on Learning Machines, this guest post describes the fitting of Covid-19 data with the SIR model and explains tricky parts of the fitting methodology and how we can mitigate some of the problems (e.g., early stopping of the algorithm or an ill-conditioned problem). It provides a very clear explanation of some tweaks to the standard model.

The code is available here.

Coronavirus : spatially smoothed decease in France and decease animation map

Published by Michael Ires, this blog post shows R code on how to use kernel weighted smoothing with arbitrary bounding areas to display a map of deaths from Covid-19 in France.

The author also published two other articles on how to build an animated map of deaths from Covid-19 in France and in Europe.

More recently, the author published an article where he uses the {geogrid} package to show the incidence of Covid19 by French departments as well as spread.

Another “flatten the COVID-19 curve” simulation… in R

Written by Javier Fernandez-Lopez, this blog post shows R code to create static plots and then simulations to demonstrate how social distancing could help to “flat the curve” of COVID-19 infections.

Tracking Covid19 Cases Throughout NJ with R

Written by Kevin Zolea, this blog post shows how to use the {gganimate} package to create an animated time series map showing how Covid19 spread throughout the U.S. state of New Jersey’s counties.

It’s fun to look at the YACM (Yet Another COVID Model)

Written by Adrian Barnett from Median Watch, all the models in this blog post are based on the excellent ordinary differential equation models by Alison Hill. They are microsimulations of those models that make heavy use of the {MicSim} package for running microsimulations in R.

Is COVID-19 as bad as all that? Yes it probably is

Written by Dr. Francis Smart from Econometrics By Simulation, this article explains clearly some of the factors that determine the infections and deaths from COVID19, with different scenarios of seriousness.

Potential Long-Term Intervention Strategies for COVID-19

On this website, several professors and members of Stanford University (Marissa Childs, Morgan Kain, Devin Kirk, Mallory Harris, Jacob Ritchie, Lisa Couper, Isabel Delwel, Nicole Nova, Erin Mordecai) developed a compartmental model of COVID-19 to evaluate possible outcomes of non-pharmaceutical interventions such as social distancing.

The website presents an introduction to the problem, the possibility to play around with the model to predict the effects of COVID intervention strategies (thanks to a Shiny app), the model details, and predictions for Santa Clara County, California.

The code is available on GitHub.

Animations in the time of Coronavirus

Written by Martin Henze from the Heads or Tails blog, this article describes how to extract and prepare the necessary data to animate the spread of the virus over time in Germany, using {gganimate} and {sf} R packages to create animated map visuals.

The author posted the dataset associated with the Germany maps to Kaggle, where he is maintaining it on a daily basis. In addition, he posted a version of the JHU US county level dataset where he added some key demographic info from the US census. This dataset is also updated daily.

COVID-19 Data and Prediction for Michigan

Written by Nagdev Amruthnath, this blog post builds and tests an exponential regression model based on (not much) State of Michigan data.

Data Visualization of COVID-19 in the US

Written by Daniel Reiff, this article examines COVID-19 growth dynamics using exponential and logistic curves.

The spread of COVID-19 across countries visualization with R

Written by Sergey Bryl, this article examines the speed and spreading of the virus across countries. One animated visualization and two stationary charts show:

how long and intensive were previous phases and
compare the effectiveness against COVID-19 for different countries

The code can be found at the end of the article.

Covid-19 and Rural Areas in the U.S

Written by Elliot Meador from Deltanomics, this blog post focuses on cases of Covid-19 in rural areas of the U.S, including whether in the South any particular state appears to be an outlier.

Covid Death Rates: Is the data correct?

Written by Sam Weiss, this post raises questions about the accuracy of reports of case numbers, in that they may fail to backfill for corrected data.

More recently, the author published two articles (part 1 and part 2) in which he finds and visualizes an association between number of people that tested positive for COVID-19 in a country and imports from China. In addition he finds that there are particular industries that are particularly correlated with COVID-19 rates.

COVID-19 Risk Heat Maps with Location Data, Apache Arrow, Markov Chain Modeling, and R Shiny

Written by Filip Stachura, this post describes Appsilon’s solution (CoronaRank) submitted to the recent Pandemic Response Hackathon. Inspired by Google’s PageRank, it uses geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using Markov Chain modeling.

COVID-19 Tracker Indonesia

Written by Dio Ariadi from DataWizArt, this post shows some very nice plots on regional variation in COVID-19 cases and deaths in Indonesia, with very neatly integrated R code for each plot.

COVID-19 Projections Using Machine Learning

Developed by Youyang Gu, this website presents an intuitive model that builds machine learning techniques on top of a classic infectious disease model to make COVID-19 infections and deaths projections for the US, all 50 US states, and more than 60 countries.

The code can be found on GitHub.

COVID-19 in Belgium: is it over yet?

Written by myself in collaboration with Prof. Niko Speybroeck and Angel Rosas-Aguirre, this article shows the evolution of the number of hospital admissions and the number of confirmed cases in Belgium (by province and at the national level).

Code of the plots is available in the article.

COVID-19 Cases by Ethnicity

Written by Tommi Suvitaival, this article investigates Covid19 deaths as a function of the percentage of a county’s population that comes from various ethnic backgrounds.

Tennessee COVID-19 Update

Written by Prof. James M. Luther, this document presents a summary of the daily data for the state of Tennessee. The author uses an interactive map, a seven-day rolling average of various metrics, and facet charts.

Simulating Coronavirus Outbreak in Cities with Origin-Destination Matrix and SEIR Model

Written by Yihui Fan, this blog post lays out a step-by-step guide on simulating and visualising the spread of Coronavirus in the Greater Tokyo Area based on Origin-Destination Matrix and SEIR Model.

Another post from the same author focuses on the effectiveness of reducing population movement in managing Coronavirus outbreak.

COVID-19 Population Mobility - How has human mobility changed under the COVID-19 Pandemic?

Written by Kelsey E. Gonzalez, this visualization aims to understand population behavior during the COVID-19 pandemic.

Data is from Cuebiq and the code is available on GitHub.

How to Build COVID-19 Data-Driven Shiny Apps in 5 minutes

Written by Emanuele Guidotti, this tutorial shows how to build a simple yet complete Shiny application using the R Package COVID19: R Interface to COVID-19 Data Hub.

In another post, the author explores the R package {COVID19} in further detail.

Analyzing data from COVID19 R package

Written by Pablo Cánovas, this blog post explores whether deaths from COVID19 are being reported accurately.

It uses data from The Human Mortality Database and the COVID-19 Data Hub.

Body Mass and Risk from COVID-19 and Influenza

Written by Prof. Radford Neal, this blog post looks at data from flu-like illnesses and some preliminary Covid19 data. The author concludes that being underweight and being seriously obese are both risk factors for serious respiratory illness.

More recently, the author published a series of other articles:

The Puzzling Linearity of COVID-19 discussing the fact that for many countries, the linear plots of total cases or total deaths go up exponentially at first, and then approach a straight line that is not horizontal
Seasonality of COVID-19, Other Coronaviruses, and Influenza: this post looks at the evidence for seasonality in influenza and the common cold Coronaviruses, and to what extent one might expect COVID-19 to also be seasonal
Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period”: this post analyzes and criticizes an earlier paper by Kissler et al. (2020). See also part 2 and part 3

HMD – Weekly Data

Written by Ronald Richman, this blog post explores the highly improbable level of deaths currently being reported using the Human Mortality Database and its recently begun special time series of weekly death data across 13 countries.

Guest posts on Chris Muir’s blog

Written by Skylar Hara, this blog post examines whether the incidence of Covid19 changed after the Governor of Hawaii issued a stay-at-home order.

Written by Ronja Steinbach, this blog post gathers data on Trump or Clinton states, percentages of minorities, and median household income to answer the following questions:

Does party affiliation of a state in the 2016 election have a significant impact on the incidence rate of the virus in that state?
Does the proportion of the population that is minority and median household income affect the incident rate of the virus across states?

Written by Akemi Santiago, this post shows a statistically different mortality rate between African-Americans in the United States and white Americans.

Written by Masha Rutenberg, this post focuses on the correlation between the population of a country and the number of confirmed infections in the country.

All authors are students of Prof. Chris Muir.

An R View into Epidemiology

Written by Joseph Rickert in R Views, this blog post tracks down R packages that help epidemiology research and shows the number of downloads in recent months for the five most popular packages.

Articles by Rob J Hyndman

Rob J. Hyndman, Professor of Statistics and Head of the Department of Econometrics and Business Statistics at Monash University (Australia), published a series of Covid19 related articles:

Forecasting COVID-19: this blog post does not use R, although Prof. Hyndman is an expert with it, but it does explain some of the problems with time series forecasting or other methods of forecasting
Why log ratios are useful for tracking COVID-19: this post presents the benefits of reporting log-scale graphics
Excess deaths for 2020: the reported COVID19 deaths in each country are often underestimated. One way to explore the true mortality effect of the pandemic is to look at “excess deaths” — the difference between death rates this year and the same time in previous years
Seasonal mortality rates: this post shows how the weekly mortality data published by the Human Mortality Database can be used to explore seasonality in mortality rates. Mortality rates are known to be seasonal due to temperatures and other weather-related effects

Turkey vs. Germany: COVID-19

Written by Selcuk Disci from DataGeeek, this article compares efforts by Turkey and Germany to control the pandemic, and tests several regression models.

Hands-on: How to build an interactive map in R-Shiny: An example for the COVID-19 Dashboard

Written by Sangmeng, this post explains how to build an interactive dashboard with Shiny with an example for the COVID-19 Dashboard.

Modelling COVID-19 in Morocco

Written by Zakariah Gassasse, this blog post presents data on cases of Covid19 in Morocco and applies the SIR model to the data.

More recently, the author published two other articles. The first article provides a short-term forecast of COVID-19 cases and deaths in Morocco by using simple but effective time-series analyses. The second article maps the outbreak to see which regions are suffering the most.

SIR models with Kermack and McKendrick

Written by Pierre Jacob, this blog post is mostly a retrospective look at the origins of the much-used SIR model.

Johns Hopkins Covid-19 Data and R

Written by Steve Miller, these blog posts (part 1 & part 2) showcase the handling of daily data of cases/deaths from Covid-19 in the U.S. published by Johns Hopkins University, and visualize moving averages of cases and deaths.

The author also published a blog post putting unemployment claims in the U.S. in perspective.

Estimating COVID-19’s $R_t$ in Real-Time

Written by Ramnath Vaidyanathan, this tutorial shows how to estimate $R_t$, the measure known as effective reproduction number, which is the number of people who become infected per infectious person at time $t$.

From static to animated time series: the tidyverse way

Written by Giulia Ruggeri, this post goes through the steps necessary to create an animated COVID-19 time series plot.

Sneak peek: new Summit data tool helps clients visualize US areas that are most heavily impacted by the COVID-19 virus

Written by Colby Ziegler, this blog post describes a tool (built primarily in R using the {leaflet}, {tidyverse}, and {tigris} packages) tracking and displaying the total number of confirmed COVID-19 cases and deaths by U.S. county, overlaid with the locations of Certified Community Development Financial Institutions (CDFIs).

How to Reproduce Financial Times Style COVID19 Daily Reporting?

Written by Peyman Kor, this blog post shows how to reproduce in R the Financial Times facet plot by country.

Visualizing COVID cases in Belgium

In this blog post, Koen Hufkens plots cases in Belgium and addresses a challenge in geo-spatial plotting.

A spatio-temporal analysis of the environmental correlates of COVID-19 incidence in Spain

Written by Antonio Paez and several co-authors, this blog post looks at weather, humidity and other factors in Spain to create a SUR model.

Another post by Antonio Paez investigates the incidence of COVID-19 in the United States using Google Community Mobility Reports.

Covid-19 Analysis

Written by Rizami Annuar, this post compares data on cases and deaths in Malaysia to other countries, including correlations.

Australian governments can choose to slow the spread of coronavirus, but they would need to act immediately

Written by Matt Cowgill, this blog post shows that Australia early on had relatively few cases, but the post argues that the country needed to act urgently. Data is from the {gtrendsR} package.

In a more recent post, using Google trends data for key words associated with unemployment, the authors trace the effects of COVID19 in Australia in the early months.

In addition to these posts, a series of other posts have been published by Stephen Duckett and Brendan Coates:

Does Covid raise everyone’s relative risk of dying by a similar amount? More evidence

Written by Prof. David Spiegelhalter (author of the great book “The Art of Statistics”), this post looks at relative mortality rates by age and gender, using data from the U.K.’s Office for National Statistics.

Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data Sources

In this post, using Johns Hopkins data, Tyler Sanders builds a virus dashboard that can be updated each day with just the click of a button as an example of how to build parameterized reports with R Markdown.

Mapping NZ cases of COVID-19

In this post, using his own choropleth package ({DHBins}) and its hexagonal bins, Thomas Lumley maps cases of COVID19 by Health Boards in New Zealand.

Visualize the Pandemic with R #COVID-19

In this post, Xinhan Qian carries out a variety of explorations with Covid19 data, including the precipitous declines in U.S. movie box office revenue and restaurant reservations.

Exploring the Temporal Evolution of COVID-19 Cases in the United States

Written by Robert Winkelman and Colin Waltz, this post shows clearly how to create animated plots of the spread of COVID19 infections in the United States.

R Data Analysis: COVID-19

Published on the blog of Sharp Sight, this series of blog posts (part 1, 2, 3, 4, 5 and 6) explain how to rename and reorder columns, standardize dates (with the {lubridate} package), merge datasets, take other preparatory steps, plot with the {ggplot2} package and finally, reproduce in R a plot that shows the relative progress of 16 countries in coping with the pandemic.

All posts use the Johns Hopkins data and data from the company’s own collection.

GA COVID-19 Reports

Based on data from the GA Department of Public Health’s report, Andrew Benesh posts a daily analysis of the U.S. state Georgia’s cases, deaths, ICU usage etc.

All his reports are posted on Medium and the code is available here.

Corona in Belgium

Published by bnosac, this post covers the early exploration of the exponential spread of Covid19, with a focus on Belgium and the Netherlands.

The Coronavirus in Italy from the Twitter’s Point of View

Written by the Kode team, these two posts (here and here) use text-mining techniques to analyze tweets in Italy early in the pandemic and on speeches by the Prime Minister of Italy regarding Covid19.

They also published an interactive dashboard (in Italian) allowing to explore the data released daily by the Civil Protection.

Use R and Tidycensus to Look at COVID-19 Risk Factors

Written by René F. Najera, this post uses US census data to identify “overcrowded” areas and considers them in terms of Covid19 risk.

Animating U.S. COVID-19 hotspots over time

Written by Nathan Chaney, this post presents an animated map of the 7-day rolling average of new COVID-19 cases in US. Code for the animated map is available directly at the end of the post.

This post is an extension of his previous post on visualizing COVID-19 in Arkansas.

Understanding COVID19 in Connecticut. It takes a town

Based on these two posts by Nathan Chaney, Chuck Powell shows in his post how to create an animated map of 7-day rolling average of new COVID19 cases per 100,000 in Connecticut (by town).

Data

2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE: this dataset is used by many resources mentioned in this article and has become the gold standard for COVID-19 modeling
World Health Organization (WHO). See also their accompanying Shiny app
COVID-19 Open Research Dataset Challenge (CORD-19) (via Kaggle)
Novel Corona Virus 2019 Dataset: Day level information on Covid-19 affected cases (via Kaggle)
COVID-19 Data from the European Centers for Disease Control
The COVID Tracking Project
Tidying the John Hopkins Covid-19 data to long format and merging some World Bank data
A Short Review of COVID-19 Data Sources
COVID-19 datasets by esri
beoutbreakprepared/nCoV2019: one of the very few non-aggregated dataset available online. Such a dataset of individual-level information on patients with confirmed COVID-19, (including their travel history, location, symptoms, reported onset and confirmation dates and basic demographics) is important to understand, among others, transmissibility, risk of geographic spread, routes of transmission and risk factors for infection
Fighting the Covid-19: All the datasets and data efforts in one place: this post gathers many relevant datasets and data efforts
A Google Sheet which helps with tracking of the local lockdowns in the UK

Other lists or collections of resources

With so many great resources about the Coronavirus, other people also collected and organized similar lists.³ Below some collections I have been fortunate enough to discover:

COVID-19 Blog Post Directory: developed by Connor Rothschild and Rees Morrison, this Shiny app lets users interactively search a collection of over 400 posts by primary topic, post title, date, and whether the post uses a particular mathematical technique or data source. See also the accompanying blog post.
COVID-19 Modelling Resources, Data and Challenges by IDEA (not only R)
GitHub repo covid19-r by Mine Cetinkaya-Rundel (only R)
Amplifying people I trust on COVID-19: written by Jeff Leek from Simply Statistics, this article is a collection of trustworthy people and experts who share good information about the COVID-19 pandemic
Some Select COVID-19 Modeling Resources and More Select COVID-19 Resources by Joseph Rickert, assembling dashboards, Shiny apps, blog posts, packages, datasets, videos and conference proceedings that pertain to the Covid19 pandemic
CovidR Contest: launched by the European R users meeting (eRum), this contest is an open-source contest and pre-conference event, featuring any work done with R around the topic of the COVID-19 pandemic
Turning COVID-19 into a data visualization exercise for your students: written by Daniela Duca, this blog post presents a variety of methods to visualize data, drawing on several blog posts, Shiny apps and dashboards
Outbreak.info is an open source tool built by Scripps Research that standardizes and aggregates COVID-19 data. The interface aggregates journal articles, preprints, datasets, clinical trials, protocols, and other resources in one place, standardizing metadata and applying NLP to make these sources searchable and more accessible. The interactive data dashboards allow for quick comparison of countries, states, counties, and metro areas, and includes an API and R Package for researchers who want to access all of the raw data

I hope that, in addition to my collection, these rich lists done by others will give you enough background materials to analyze the outbreak of COVID-19 on your own (or at least some ideas)!

Non-english resources

This section may be of interested to only a limited number or people, but still, there are great resources in languages other than English. See a collection of them below listed by language:

Japanese:
- Coronavirus infection bulletin: original version of this dashboard
German:
- Developed by Prof. Dr. Helmut Küchenhoff, this CoronaMaps presents the situation of the Coronavirus in the world, in Europe and in Germany via a map and a table
Spanish:
- GitHub repository with official government data and R code used to extract it, see here and here
- COVID-19 en Chile: this Shiny app shows the accumulated confirmed contagion cases and provides an estimate of the growth rate for each municipality
- Developed by Que Oferton, this Shiny app provides an overview of the 2019 Novel Coronavirus COVID-19 epidemic in Central America, including statistics, forecast, SIR and SEIR models. The data and dashboard are refreshed on a daily basis
French:
- Written by Arthur Charpentier, this blog post shows how to quantify excess mortality using french mortality data

Conclusion

Thanks for reading.

I hope you will find these R resources on the COVID-19 Coronavirus useful. Feel free to let me know in the comments if I missed one.

A special thanks to Rees Morrison for his tremendous work on collecting and organizing several articles, which greatly helped in improving the section about blog posts. Read his articles (part 1 and 2) presenting a descriptive analysis of all the posts collected.

Although I have carefully read all resources, inclusion on the list does not mean that I endorse the findings. Moreover, some of the analyses, code, dashboards, packages or datasets might be out of date, so these should not be viewed, by default, as current findings. If you are the author of one of these resources, do not hesitate to contact me if you see any inconsistency or if you would like to remove it from this article.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

References

de Vlas, Sake J, and Luc E Coffeng. 2020. “A Phased Lift of Control: A Practical Strategy to Achieve Herd Immunity Against Covid-19 at the Country Level.” medRxiv.

Kissler, Stephen M, Christine Tedijanto, Edward Goldstein, Yonatan H Grad, and Marc Lipsitch. 2020. “Projecting the Transmission Dynamics of SARS-CoV-2 Through the Postpandemic Period.” Science 368 (6493): 860–68.

The package has also been the subject of a preprint.↩︎
See more information about this epidemiological model in this post by Marc Choisy.↩︎
Note that unlike my list, collections by others may include resources on COVID-19 using other tools than R.↩︎

Coronavirus on Stats and R

EpiLPS for estimation of incubation times

Motivation

Coarse data

Simulated example

Real data example

References

Paper: 'EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number'

Introduction

Motivation

Getting started

A simulated example

Smoothing the epidemic curve and estimating \(\mathcal{R}_t\)

USA hospitalization data

Conclusion

References

COVID-19 in Belgium: is it over yet?

Introduction

New hospital admissions

Overall

By period

Zooming in

Patients in hospitals

Patients in intensive care

Confirmed cases

By province

By age group and sex

Static

Dynamic

By age group, sex and province

Conclusion

A package to download free Springer books during Covid-19 quarantine

Update

Introduction

Installation

Download all books at once

Create a table of Springer books

Download only specific books

By title

By author

By subject

Improvements

Acknowledgments

Conclusion

COVID-19 in Belgium

Introduction

Top R resources on Coronavirus

Coronavirus dashboard for your own country

Motivations, limitations and structure of the article

Analysis of Coronavirus in Belgium

A classic epidemiological model: the SIR model

Fitting a SIR model to the Belgium data

Reproduction number \(R_0\)

Using our model to analyze the outbreak if there was no intervention

More summary statistics

Additional considerations

Ascertainment rates

More sophisticated models

Modelling the epidemic trajectory using log-linear models

Estimating changes in the effective reproduction number \(R_e\)

More sophisticated projections

Conclusion

References

How to create a simple Coronavirus dashboard specific to your country in R?

Introduction

Top R resources on Coronavirus

Coronavirus dashboard: the case of Belgium

How to create your own Coronavirus dashboard

Additional notes

Data

Open source

Accuracy

Publish your dashboard

Conclusion

Top 100 R resources on COVID-19 Coronavirus

R Shiny apps and dashboards

Coronavirus tracker

Coronavirus dashboard from the {coronavirus} package

Visualization of Covid-19 Cases

Modeling COVID-19 Spread vs Healthcare Capacity

Coronavirus dashboard from the `{coronavirus} package`

`{nCov2019}`

`{coronavirus}`

`{tidycovid19}`

`{covdata}`

`{covid19italy}`

`{COVID19}`

`{COVOID}`

`{cdccovidview}`

`{babsim.hospital}`

`{EpiLPS}`

COVID-19 Data Analysis with `{tidyverse}` and `{ggplot2}`