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

TL;DR

This paper compares CDC ensemble COVID-19 fatality forecasts with a simple Euler method benchmark, finding that the simple method performs comparably on short-term horizons and better on longer ones, highlighting the value of local rate-based forecasts.

Contribution

It introduces a model-free Euler benchmark for COVID-19 fatality forecasting and demonstrates its effectiveness relative to complex ensemble models over different time horizons.

Findings

Euler forecasts are as accurate as CDC ensemble forecasts at one-week horizon.

Euler method outperforms ensemble forecasts on longer horizons.

Simple, data-light forecasting methods can be effective for epidemic prediction.

Abstract

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 U.S., 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. We collected data on CDC ensemble forecasts of COVID-19 fatalities in the United States, and 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. Our results show that CDC ensemble forecasts are not more accurate than ``Euler'' forecasts on short-term forecasting horizons of one week. However, CDC ensemble forecasts show a better performance on longer forecasting horizons. 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., human mobility and contact patterns) and high-performance computing systems.