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–infected–recovered (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 COVID19 and find that the multiwave SERID model is able to outperform the majority of CDC models in long-term predictions.
Contributors: Divya Singhvi, Omar Skali Lami, Leann Thayaparan