A data-driven test approach to identify COVID-19 surge phases: an alert-warning tool

Arianna Agosto () and Paola Cerchiello ()
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Arianna Agosto: University of Pavia
Paola Cerchiello: University of Pavia

No 214, DEM Working Papers Series from University of Pavia, Department of Economics and Management

Abstract: The effective monitoring of the pandemic emergency and, specifically, the early detection of surge phases are crucial to define proper health policies. We propose a statistical testing approach to identify the acceleration in contagion growth that potentially marks the start of new waves, based on the study of the reproduction rate dynamics. The proposed method can be considered as a supplementary early warning system that can assist policymakers in the attempt to anticipate and tailor countermeasures. It can also be used as an ex-post tool to date-stamp surge phases and evaluate the impact of the implemented strategies on their timing. The effectiveness of our approach is exemplified on ten countries’ contagion data, reaching robust and insightful results in assessing the timing and severity of COVID-19 surge phases.

Keywords: COVID-19 pandemics; Reproduction rate; Explosivity tests; Epidemic surge detection. (search for similar items in EconPapers)
Pages: 17
Date: 2023-10
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