Quantifying the Regional Disproportionality of COVID-19 Spread (preprint)

ABSTRACT

BackgroundThe COVID-19 pandemic has caused serious health problems and has had major economic and social consequences worldwide. Understanding how infectious diseases spread can help mitigating the social and economic impact. ObjectiveThe study focuses to capture the degrees of disproportionality in prevalence rates of infectious disease across different regions over time. MethodsWe analyze the numbers of daily COVID-19 confirmed cases in the United States collected by Johns Hopkins University over 1100 days since the first reported case in January 2020 in order to assess quantitatively the disproportionality of the confirmed cases using the Theil index, a measure of imbalance used in economics. Results: Our results reveal a dynamic pattern of interregional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progresses. ConclusionsThe combined monitoring of this indicator and the confirmed cases is crucial for understanding regional differences in infectious diseases and for effective planning of response and resource allocation.