Author: Fu-Chang Hu; Fang-Yu Wen
Title: The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China Document date: 2020_4_17
ID: nlpeyh5e_1
Snippet: the sooner the proper actions were taken against the spread of the coronavirus, the smaller the size of the COVID-19 epidemic and its damage would likely be. Since the coronavirus is highly transmittable by droplets, more stringent control measures such as fast and mass testing, rigorous contact tracing, large-scale isolations, mandatory quarantines, travel restrictions or bans, border closing, social distancing, school closings, stay-at-home ord.....
Document: the sooner the proper actions were taken against the spread of the coronavirus, the smaller the size of the COVID-19 epidemic and its damage would likely be. Since the coronavirus is highly transmittable by droplets, more stringent control measures such as fast and mass testing, rigorous contact tracing, large-scale isolations, mandatory quarantines, travel restrictions or bans, border closing, social distancing, school closings, stay-at-home orders, and long-term lockdowns may be needed to contain the epidemic ultimately. Almost the whole world is now in the battle against the coronavirus as the global numbers of confirmed COVID-19 cases and deaths continue to rise speedily. methods specific for this epidemic, we tried to find an available easy-to-use tool to monitor the progress of the ongoing COVID-19 epidemic as soon as possible. As listed on the Comprehensive R Archive Network (CRAN) (https://cran.r-project. org/), several R packages might be used to compute basic reproduction numbers of an epidemic in R, including argo, epibasix, EpiCurve, EpiEstim, EpiILM, EpiILMCT, epimdr, 3 epinet, epiR, EpiReport, epitools, epitrix, incidence, mem, memapp, R0, and surveillance. We chose the incidence (version 1.7.0) and EpiEstim (version 2.2-1) packages to estimate R0(t) in R during the ongoing COVID-19 epidemic in China due to their methodological soundness and computational simplicity for rapid analysis. 4, 5 Our R code was listed in Supplementary Appendix 2 for check and reuses.
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