Taylor & Francis Group
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Nonparametric estimation and inference for spatiotemporal epidemic models

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posted on 2021-11-20, 19:00 authored by Yueying Wang, Myungjin Kim, Shan Yu, Xinyi Li, Guannan Wang, Li Wang

Epidemic modelling 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 modelling framework to study the spatial-temporal pattern in the spread of infectious diseases. We propose a quasi-likelihood approach via the penalised spline approximation and alternatively reweighted least-squares technique to estimate the model. The proposed estimators are consistent, and the asymptotic normality is established for the constant coefficients. Utilizing spatiotemporal analysis, our proposed model enhances the dynamics of the epidemiological mechanism and dissects the spatiotemporal structure of the spreading disease. We evaluate the numerical performance of the proposed method through a simulation example. Finally, we apply the proposed method in the study of the devastating COVID-19 pandemic.


Myungjin Kim's research was partially supported by National Science Foundation (Division of Computing and Communication Foundations) award CCF-1934884, DMS-2135493, DMS-1916204 and Laurence H. Baker Center for Bioinformatics & Biological Statistics. Shan Yu's research was partially supported by the Iowa State University Plant Sciences Institute Scholars Program. Li Wang's research was partially supported by National Science Foundation (Division of Mathematical Sciences) award DMS-1916204 and DMS-2135493.