Nonparametric estimation and inference for spatiotemporal epidemic models

Yueying Wang, Myungjin Kim, Shan Yu, Xinyi Li, Guannan Wang, Li Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
JournalJournal of Nonparametric Statistics
DOIs
StateAccepted/In press - 2021

Keywords

  • 62G05
  • 62G08
  • 62G20
  • Coronavirus
  • nonparametric modelling
  • partially linear models
  • spatial epidemiology
  • splines
  • varying coefficient models

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