Coronavirus COVID-19 outbreak statistics and forecast
Authors: Steven Ge, Guangchuang YU
Working with Shiny more than 1 year
Abstract: To provide direct access to real-time epidemiological data on COVID-19 pandemic, we developed an R package, nCov2019. This open-source software aggregates data from four different sources. We also developed Shiny web apps in both English and Chinese. These apps can also be run locally from Rstudio. Our web app enables users to select their regions of interest and check both the historical and real-time data. We also conduct time-series forecasting for different countries on this coronavirus outbreak.
Full Description: To provide convenient access to epidemiological data on the COVID-19 coronavirus outbreak, we developed an R package, nCov2019 (GitHub - GuangchuangYu/nCov2019: query stats of infected coronavirus cases). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts for different countries. These analytics tools could be useful in informing and enabling researchers, public health officials, and the public.
Built with the RStudio Shiny framework, these apps contain a simple forecast module. We first converted the log-transformed numbers of cases or deaths as time-series data, then used the exponential smoothing method (ets) in the R package forecast to forecast the total cases. On February 7, 2020, this simple model predicted that the death toll would reach 2000 in ten days, a staggering number at the time that later materialized, unfortunately. We also converted the raw number of cases as percent daily changes and conducted a similar forecast. Interestingly, daily percent changes in both confirmed cases and deaths in China are decreasing linearly except for a few outliers.
Some of our map files too large to be hosted on the Rstudio cloud.
Category: Healthcare
Keywords: covid-19, public health, epidemiology
Shiny app: http://www.bcloud.org/e/
Repo: GitHub - gexijin/covid
RStudio Cloud: N/A
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