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.

翻译：传染病传播的短期预报是风险评估和公共卫生决策的关键组成部分。尽管已有多种短期预报模型，但关于其相对性能的开放性问题依然存在。本文对基于更新方程的主流机理模型的短期概率预报与统计时序模型的预报进行了比较。实证比较基于美国六大州在首个大流行年度的每日COVID-19发病率数据。研究发现，统计时序模型的概率预报整体上至少与机理模型的预报同样准确。此外，统计时序模型能更好地捕捉波动性。结果表明，通过假设疾病动态整合到机理模型中的领域知识，并不能改善疾病发病率的短期预报。但我们注意到，预报往往只是众多目标之一，因此机理模型仍然重要，例如用于模拟疫苗影响或新变异株的出现。