There are several potential sources of data which have been abstracted and collated from such governmental sites. Various national and provincial/governmental web sites in affected countries provide detailed summary data on incident cases, recovered cases and deaths due to the virus, but these data tend to be in the form of counts embedded in (usually non-English) text. Obtaining detailed, accurate and current data for the COVID-19 epidemic is not as straightforward as it might seem. This post is based on two much longer and more detailed blog posts I have published in the last few weeks on the same topic, but it uses US data.
This post doesn’t seek to provide a review of the available packages – rather it illustrates the utility of a few of the excellent packages available in the R Epidemics Consortium ( RECON) suite, as well as the use of base R and tidyverse packages for data acquisition, wrangling and visualization. In fact, R is one of the tools of choice for outbreak epidemiologists, and a quick search will yield many R libraries on CRAN and elsewhere devoted to outbreak management and analysis. Dashboards of global spread are beginning to light up like Christmas trees.įor R users, an obvious question is: “Does R have anything to offer in helping to understand the situation?”. Every news report is dominated by alarming, and ever-growing cumulative counts of global cases and deaths due to COVID-19. As I write this on 4th March, 2020, the world is on the cusp of a global COVID-19 pandemic caused by the SARS-Cov2 virus.
0 kommentar(er)
