The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks. We present an international, prospective evaluation of our models’ performance across all states and counties in the USA and prefectures in Japan. Nationally, incident mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths during prospective deployment remained consistently <8% (US) and <29% (Japan), while cumulative MAPE remained <2% (US) and <10% (Japan). We show that our models perform well even during periods of considerable change in population behavior, and are robust to demographic differences across different geographic locations. We further demonstrate that our framework provides meaningful explanatory insights with the models accurately adapting to local and national policy interventions. Our framework enables counterfactual simulations, which indicate continuing Non-Pharmaceutical Interventions alongside vaccinations is essential for faster recovery from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions.

COVID-19 大流行凸显了全球对可靠疾病传播模型的需求。我们提出了一个人工智能增强的预测模型框架，该框架可以每日预测未来 4 周内确诊的 COVID-19 死亡人数、病例数和住院人数。我们对我们的模型在美国所有州和县以及日本县的表现进行了国际前瞻性评估。在全国范围内，预测部署期间与 COVID-19 相关的死亡的事件平均绝对百分比误差 (MAPE) 始终保持 <8%（美国）和 <29%（日本），而累积 MAPE 保持 <2%（美国）和 <10% （日本）。我们表明，即使在人口行为发生重大变化的时期，我们的模型也表现良好，并且对不同地理位置的人口差异具有稳健性。我们进一步证明，我们的框架提供了有意义的解释性见解，模型准确地适应地方和国家政策干预。我们的框架可以进行反事实模拟，这表明在疫苗接种的同时持续进行非药物干预对于更快地从大流行中恢复至关重要，延迟干预措施的应用会产生不利影响，并允许探索不同疫苗接种策略的后果。 COVID-19 大流行仍然是全球紧急情况。面对未来的重大挑战，这里提出的方法有可能为关键决策提供信息。