Abstract：
In mid-2022, New York City (NYC) became the epicenter of the US mpox outbreak. We provided real-time mpox case forecasts to the NYC Department of Health and Mental Hygiene to aid in outbreak response. Forecasting methodologies evolved as the epidemic progressed. Initially, lacking knowledge of at-risk population size, we used exponential growth models to forecast cases. Once exponential growth slowed, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Retrospectively, we explored if forecasts could have been improved using an SEIR model in place of our early exponential growth model, with or without knowing the case detection rate. Early forecasts from exponential growth models performed poorly, as 2-week mean absolute error (MAE) grew from 53 cases/week (July 1-14) to 457 cases/week (July 15-28). However, when exponential growth slowed, providing insight into susceptible population size, an SEIR model was able to accurately predict the remainder of the outbreak (7-week MAE: 13.4 cases/week). Retrospectively, we found there was not enough known about the epidemiological characteristics of the outbreak to parameterize an SEIR model early on. However, if the at-risk population and case detection rate were known, an SEIR model could have improved accuracy over exponential growth models early in the outbreak.
摘要：
2022年中期，纽约市（NYC）成为美国水痘爆发的中心。我们向纽约市卫生和精神卫生部提供了实时的水痘病例预测，以帮助应对疫情。预测方法随着疫情的发展而发展。最初，缺乏对高危人群规模的了解，我们使用指数增长模型来预测案例。一旦指数增长放缓，我们使用了一个易感暴露感染恢复(SEIR)模型。回顾过去,我们探索了使用SEIR模型代替我们早期的指数增长模型是否可以改善预测，知道或不知道案件检测率。来自指数增长模型的早期预测表现不佳，2周平均绝对误差（MAE）从53例/周（7月1日至14日）增加到457例/周（7月15日至28日）。然而,当指数增长放缓时，提供易感人群规模的洞察，SEIR模型能够准确预测疫情的其余部分(7周MAE:13.4例/周).回顾过去,我们发现对疫情的流行病学特征了解不足,无法在早期对SEIR模型进行参数化.然而,如果已知高危人群和病例检出率，与爆发初期的指数增长模型相比，SEIR模型可以提高准确性。
