Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

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One of the greatest challenges of the COVID-19 pandemic has been the way evolving regulation, information, and sentiment have driven waves of the disease. Traditional epidemiology models, such as the SIR model, are not equipped to handle these behavioral-based changes. We propose a novel multiwave susceptible-infectedrecovered (SIR) model, which can detect and model the waves of the disease. We bring together the SIR model's compartmental structure with a change-point detection martingale process to identify new waves. We create a dynamic process where new waves can be flagged and learned in real time. We use this approach to extend the traditional susceptible-exposed-infected-recovered-dead (SEIRD) model into a multiwave SEIRD model and test it on forecasting COVID-19 cases from the John Hopkins University data set for states in the United States. We find that compared to the traditional SEIRD model, the multiwave SEIRD model improves mean absolute percentage error (MAPE) by 15%-25% for the United States. We benchmark the multiwave SEIRD model against top performing Center for Disease Control (CDC) models for COVID-19 and find that the multiwave SERID model is able to outperform the majority of CDC models in long-term predictions.

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