Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
This research provides insights into the effectiveness and timing of policy interventions for public health officials and policymakers during pandemics, by quantifying the lag between NPIs and their impact on disease spread.
This paper investigates the time lag between the implementation of non-pharmaceutical interventions (NPIs) and their effect on COVID-19 case numbers. Using machine learning and drift detection methods across 9 European countries and 28 US states, the study found an average lag of over two weeks between NPI enactment and a detectable change in case numbers.
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.