Growth rate and acceleration analysis of the COVID-19 pandemic reveals the effect of public health measures in real time

Yuri Tani Utsunomiya; Adam Taiti Harth Utsunomiya; Rafaela Beatriz Pintor Torrecilha; Silvana Cassia Paulan; Marco Milanesi; Jose Fernando Garcia

The World Health Organization (WHO) officially declared Coronavirus Disease (COVID-19) a global pandemic on March 11 th 2020 (1) . The disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) (2,3), which seems to have first emerged in Wuhan, China on December 12 th 2019 (4, 5) . Worldwide dissemination has been extremely rapid, and by the time this study was completed (April 2 nd 2020) a total of 928,437 cases and 46,891 deaths had been reported across 204 countries and territories according to data from the European Center for Disease Prevention and Control (ECDC) (6) . Approximately 86% of all cases are estimated to have been undocumented prior to the cordon sanitaire in China (7) , which suggests that the disease might be also substantially under-reported in other countries. Nevertheless, partial COVID-19 prevalence data are still an invaluable resource to help monitoring and controlling the disease. In particular, extracting daily estimates of growth rate (cases/day) and acceleration (cases/day²) in disease dissemination from real-time case reports can be decisive for an effective and promptly action to restrain further contagion. Here we report the development of a simple framework dedicated to the real-time analysis of COVID-19 prevalence. This framework was built using a combination of Moving Regression (MR) and Hidden Markov Model (HMM), and was deployed as a Shiny (8) application in R (9) . Here we show the utility of that framework in the analysis of publicly available COVID-19 case reports that are updated daily by the ECDC.