We introduce a minimalist outbreak forecasting model that combines data-driven parameter estimation with variational data assimilation. By focusing on the fundamental components of nonlinear disease transmission and representing data in a domain where model stochasticity simplifies into a process with independent increments, we design an approach that only requires four core parameters to be estimated. We illustrate this novel methodology on COVID-19 forecasts. Results include case count and deaths predictions for the US and all of its 50 states, the District of Columbia, and Puerto Rico. The method is computationally efficient and is not disease- or location-specific. It may therefore be applied to other outbreaks or other countries, provided case counts and/or deaths data are available.
翻译:我们引入了将数据驱动的参数估计与变异数据同化相结合的最起码的爆发预测模型。通过侧重于非线性疾病传播的基本组成部分,并代表模型随机性简化为独立递增过程的领域内的数据,我们设计了只要求估计四个核心参数的方法。我们用COVID-19预测来说明这种新方法。结果包括美国及其所有50个州、哥伦比亚特区和波多黎各的病例数和死亡预测。这种方法是计算效率高的,不是针对疾病或地点的。因此,只要有病例数和/或死亡数据,它可以适用于其他疾病爆发或其他国家。