Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic

TL;DR

This paper uses machine learning and concept drift detection to analyze the impact of COVID-19 policy interventions across multiple regions, revealing an average two-week lag between policy implementation and changes in case numbers.

Contribution

It introduces a novel application of drift detection methods to measure the effectiveness and timing of COVID-19 policy interventions.

Findings

Average two-week lag between NPIs and case number drift

Drift detection can quantify policy impact timing

Method applicable across different regions

Abstract

As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it remains unclear which effect these measures, so-called non-pharmaceutical interventions (NPIs), have on the spread of the virus. In this article, we use machine learning and apply drift detection methods in a novel way to predict the time lag of policy interventions with respect to the development of daily case numbers of COVID-19 across 9 European countries and 28 US states. Our analysis shows that there are, on average, more than two weeks between NPI enactment and a drift in the case numbers.