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This article attempts to solve a puzzle regarding the sensitivity of analysis results of COVID-19 infection and fatality. Our findings suggest that measurement errors, statistical significance of explanatory variables, and regional differences play a crucial role in the sensitivity of results. The significance of political stability, governance indicators, medical resources, demographic features is inconsistent and depends on the source of data, the choice of the time period, and region. This article also provides evidence that careful data screening and use of the moving average technique can alleviate the sensitivity issue and produce fairly robust results. We conclude that social science research on COVID-19 should not underestimate the issue of data quality and should refine raw data to minimize random error. If the sources of measurement error are not carefully managed and intensive statistical tests of sensitivity are not verified, data quality will end being subjected to the skepticism of evidence-based policy making.

Sensitivity of COVID-19 analysis results focusing on data source, time, and region