JDS Journal of Data Science 1683-8602 1680-743X 1680-743X School of Statistics, Renmin University of China JDS1002 10.6339/21-JDS1002 Statistical Data Science Estimating the Number of Infected Cases in COVID-19 Pandemic YanDonghuidyan@umassd.edu1∗ XuYing2 WangPei3 Mathematics and Data Science, University of Massachusetts, Dartmouth, MA, USA Indigo Agriculture Inc, Boston, MA, USA Icahn School of Medicine at Mount Sinai, NYC, NY, USA Corresponding author. Email: dyan@umassd.edu. 20212322021192348364 Supplementary Material

In the online supplementary, we provide all R scripts and datasets used to produce the figures and results reported in the paper. All the R scripts are placed in the main directory of a .zip archive, along with a dataset for 2020 US population and a README document that briefly describes the R scripts and the datasets. Data collected as of Apr 20, 2020 and Aug 31, 2020 are placed in respective subdirectories; there is a separate time series dataset for each US state with such information as the report case numbers, death tolls etc.

 7122020 1012021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

It is widely acknowledged that the reported numbers of infected cases with COVID-19 were not complete. A structured approach is proposed where we distinguish cases reflected later in the numbers of confirmed cases and those with mild or no symptoms thus not captured by any systems at all. The number of infected cases in the US is estimated to be 220.54% of that reported as of Apr 20, 2020. This implies an overall infection ratio of 0.53%, and a case mortality rate at 2.85% which is close to the 3.4% suggested by WHO in March 2020.

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