Nonparametric comparison of epidemic time trends: the case of COVID-19

TL;DR

This paper introduces new nonparametric inference methods to compare COVID-19 epidemic time trends across countries, providing a statistically rigorous way to detect differences in virus spread patterns.

Contribution

The paper develops novel nonparametric methods for comparing epidemic time trends across countries, addressing a gap in statistical tools for epidemic analysis.

Findings

Detected significant differences in COVID-19 spread patterns across European countries.

Validated the effectiveness of the new inference methods in empirical comparisons.

Provided insights into the heterogeneity of epidemic development across regions.

Abstract

The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.