From policy to prediction: Forecasting COVID-19 dynamics under imperfect vaccination

TL;DR

This study extends a COVID-19 forecasting model to include vaccination effects and policy impacts, demonstrating improved prediction accuracy and identifying key policy influences on disease spread.

Contribution

The paper introduces a modified ODE-based forecasting model that incorporates vaccination status and breakthrough cases, enhancing prediction accuracy during the post-vaccination period.

Findings

Inclusion of policy data improves forecast accuracy from 34% to 21%.

Restrictions on gatherings remain the most influential predictor.

The model effectively predicts COVID-19 cases up to 35 days ahead.

Abstract

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model [WWR+]. In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of 34%, which is further improved to 21% if combined with human mobility data. Moreover, similar to the pre-vaccination study, the most influential predictor variable remains the policy of restrictions on gatherings. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future.