On the accuracy of short-term COVID-19 fatality forecasts

Abstract

Background: Forecasting new cases, hospitalizations, and disease-induced deaths is an important part of infectious disease surveillance and helps guide health officials in implementing effective countermeasures. For disease surveillance in the US, the Centers for Disease Control and Prevention (CDC) combine more than 65 individual forecasts of these numbers in an ensemble forecast at national and state levels. A similar initiative has been launched by the European CDC (ECDC) in the second half of 2021. Methods: We collected data on CDC and ECDC ensemble forecasts of COVID-19 fatalities, and we compare them with easily interpretable “Euler” forecasts serving as a model-free benchmark that is only based on the local rate of change of the incidence curve. The term “Euler method” is motivated by the eponymous numerical integration scheme that calculates the value of a function at a future time step based on the current rate of change. Results: Our results show that simple and easily interpretable “Euler” forecasts can compete favorably with both CDC and ECDC ensemble forecasts on short-term forecasting horizons of 1 week. However, ensemble forecasts better perform on longer forecasting horizons. Conclusions: Using the current rate of change in incidences as estimates of future incidence changes is useful for epidemic forecasting on short time horizons. An advantage of the proposed method over other forecasting approaches is that it can be implemented with a very limited amount of work and without relying on additional data (e.g., data on human mobility and contact patterns) and high-performance computing systems.