A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan

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Abstract 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 and we present an international, prospective evaluation of our models' performance across all states and counties in the USA and prefectures in Japan. National mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths before and after prospective deployment remained consistently <2% (US) and <10% (Japan). Average statewide (US) and prefecture wide (Japan) MAPE was 6% and 26% respectively (14% when looking at prefectures with more than 10 deaths). We show that our models perform well even during periods of considerable change in population behavior, and that it is 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.

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