Estimating the Growth Rate and Doubling Time for Short-Term Prediction and Monitoring Trend During the COVID-19 Pandemic with a SAS Macro

Stanley Xu; Christina Clarke; Susan Shetterly; Komal Narwaney

In December 2019, an outbreak of coronavirus disease (COVID-19) caused by the novel coronavirus (SARS-CoV-2) began in Wuhan, China and has now spread across the world [1, 2] . In the United States, the cumulative number of identified COVID-19 cases was 186,101 as of March 31st, 2020; among the identified cases, 3603 died [3] . To slow the spread of COVID-19, federal and local governments have issued mitigation measures such as case isolation, quarantine, school closures and closing non-essential businesses. The COVID-19 pandemic imposes tremendous challenges to the US health care system, particularly given concerns that the need for hospital beds and ICU beds could exceed capacity [4] [5] [6] . Predicting the future numbers of COVID-19 cases and healthcare utilization is critical for governments and health care systems preparation plans [4, 6, 7] . Two useful and critical quantities for prediction are the growth rate [8] and the doubling time of number of events [9] . The growth rate is the percent change of daily events (e.g, COVID-19 cases, number of patients hospitalized or number of deaths). The doubling time is the length of time required to double the number of daily events.