The Unassuming Economist

New VoxEU.org post by Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini, Paulo Santos Monteiro published on 16 February 2022.

“For policymakers and healthcare providers, prediction of the evolution of an epidemic is extremely important (Manski 2020, Castle et al. 2020). Timely and reliable projections are required to assist health authorities, and the community in general, in coping with an infection surge and to inform public health interventions such as enforcing (or facilitating) local or national lockdowns (Heap et al. 2020). Weekly forecasts of the evolution of the COVID-19 pandemic generated by various independent institutions and research teams have been collected by the Centers for Disease Control and Prevention (CDC) in the US.¹ These forecasts are intended to inform the decision-making process for public health interventions by predicting the impact of the COVID-19 pandemic for up to four weeks. However, this wealth of forecasts also poses a problem: how to act when confronted with heterogeneous forecasts and, in particular, how to select the most reliable projections.

Forecasting teams, methods, and assumptions

The forecasting teams include data scientists, epidemiologists, and statisticians. They use different methodologies and approaches (e.g. the susceptible-exposed-infectious-recovered (SEIR), Bayesian, and deep learning models) and combine a range of data sources and assumptions concerning the impact of non-pharmaceutical interventions on the spread of the epidemic (such as social distancing and the use of face coverings). In Table 1, we report the eight teams that continuously submitted their predictions since the start of the pandemic, for the period 20 June 2020 to 20 March 2021. “

Read more here.