  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.
