Nonparametric estimation and inference for spatiotemporal epidemic models
Description
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.
Publication Date
1-1-2021
Publisher
figshare Academic Research System
DOI
10.6084/m9.figshare.17056307
Document Type
Data Set
Recommended Citation
Yu, Shan; Li, Xinyi; Kim, Myungjin; Wang, Yueying; Wang, Guannan; Wang, Li (2021), "Nonparametric estimation and inference for spatiotemporal epidemic models", figshare Academic Research System, doi: 10.6084/m9.figshare.17056307
https://doi.org/10.6084/m9.figshare.17056307
Identifier
10.6084/m9.figshare.17056307
Embargo Date
1-1-2021