COVID-19 Pandemic Data Analytics: Data Heterogeneity, Spreading Behavior, and Lockdown Impact

Abstract

Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible speed of spread shows the vulnerability of a globalized and networked world. The first months of the pandemic were characterized by heavy burden on health systems and severe restrictions on public life within a lot of countries, like educational system shutdown, public traffic system breakdown or a comprehensive lockdown. The goal of the presented research study is the analysis of the development of the occurrence of infection within the early COVID-19 pandemic time period (December 2019 – June 2020), using the Weibull distribution model, the Verhulst saturation model and Polynomial approaches. The analyses of the pandemic data contains uncertainty of data acquisition, pandemic spreading and trend behavior, analysis of the lockdown impact and the developing of saturation effects. These topics are discussed based on the data sets of China (province Hubei), Italy, Germany, Sweden and Japan. Despite data uncertainty, the impact of the different lockdown strategies can be seen based on model parameter interpretation: Strong, significant impact (decreasing spreading speed) in Germany (lockdown: “distance regulations”) and Italy (lockdown);Japan and Sweden show a lower significant impact maybe caused by soft lockdown and society influences. Furthermore, the impact of the lockdown is visible by observation of the deviation development regarding trend respectively prognosis models (before lockdown) versus realitas (after lockdown). © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.