Spatiotemporal Dynamics, Nowcasting and Forecasting of COVID-19 in the United States

TL;DR

This paper introduces a novel spatiotemporal epidemic modeling framework that combines classic models with advanced statistical techniques to improve COVID-19 spread prediction at county and state levels in the U.S.

Contribution

It develops a new space-time epidemic model using penalized splines and reweighted least squares, enhancing disease spread understanding and forecasting accuracy.

Findings

Accurate short-term and long-term COVID-19 forecasts at county and state levels

Model effectively incorporates control measures and local features

Bootstrap-based uncertainty quantification improves prediction reliability

Abstract

Epidemic modeling is an essential tool to understand the spread of the novel coronavirus and ultimately assist in disease prevention, policymaking, and resource allocation. In this article, we establish a state of the art interface between classic mathematical and statistical models and propose a novel space-time epidemic modeling framework to study the spatial-temporal pattern in the spread of infectious disease. We propose a quasi-likelihood approach via the penalized spline approximation and alternatively reweighted least-squares technique to estimate the model. Furthermore, we provide a short-term and long-term county-level prediction of the infected/death count for the U.S. by accounting for the control measures, health service resources, and other local features. Utilizing spatiotemporal analysis, our proposed model enhances the dynamics of the epidemiological mechanism and dissects the spatiotemporal structure of the spreading disease. To assess the uncertainty associated with the prediction, we develop a projection band based on the envelope of the bootstrap forecast paths. The performance of the proposed method is evaluated by a simulation study. We apply the proposed method to model and forecast the spread of COVID-19 at both county and state levels in the United States.