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Nonparametric estimation and inference for spatiotemporal epidemic models

Author

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

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.

Suggested Citation

  • Yueying Wang & Myungjin Kim & Shan Yu & Xinyi Li & Guannan Wang & Li Wang, 2022. "Nonparametric estimation and inference for spatiotemporal epidemic models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(3), pages 683-705, July.
  • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:3:p:683-705
    DOI: 10.1080/10485252.2021.1988084
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    Cited by:

    1. Giordano, Francesco & Milito, Sara & Parrella, Maria Lucia, 2023. "Linear and nonlinear effects explaining the risk of Covid-19 infection: an empirical analysis on real data from the USA," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).

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