A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
This addresses the problem of selecting accurate forecasting models for infectious disease spread, relevant to public health decision-makers, but is incremental as it compares existing methods on new data.
The study compared short-term probabilistic forecasts for COVID-19 incidence using mechanistic and statistical time series models, finding that statistical models were at least as accurate on average and better captured volatility, with no improvement from domain knowledge in mechanistic models.
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models. Our empirical comparison is based on data of the daily incidence of COVID-19 across six large US states over the first pandemic year. We find that, on average, probabilistic forecasts from statistical time series models are overall at least as accurate as forecasts from mechanistic models. Moreover, statistical time series models better capture volatility. Our findings suggest that domain knowledge, which is integrated into mechanistic models by making assumptions about disease dynamics, does not improve short-term forecasts of disease incidence. We note, however, that forecasting is often only one of many objectives and thus mechanistic models remain important, for example, to model the impact of vaccines or the emergence of new variants.