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Short-term forecasts of infectious disease spread are a critical component inrisk evaluation and public health decision making. While different models forshort-term forecasting have been developed, open questions about their relativeperformance remain. Here, we compare short-term probabilistic forecasts ofpopular mechanistic models based on the renewal equation with forecasts ofstatistical time series models. Our empirical comparison is based on data ofthe daily incidence of COVID-19 across six large US states over the firstpandemic year. We find that, on average, probabilistic forecasts fromstatistical time series models are overall at least as accurate as forecastsfrom mechanistic models. Moreover, statistical time series models bettercapture volatility. Our findings suggest that domain knowledge, which isintegrated into mechanistic models by making assumptions about diseasedynamics, does not improve short-term forecasts of disease incidence. We note,however, that forecasting is often only one of many objectives and thusmechanistic models remain important, for example, to model the impact ofvaccines or the emergence of new variants.

A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models