Nonparametric estimation and inference for spatiotemporal epidemic models
Nonparametric estimation and inference for spatiotemporal epidemic models
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DOI：
10.1080/10485252.2021.1988084
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发表时间：
2021-11
影响因子：
1.2
通讯作者：
Yueying Wang;Myungjin Kim;Shan Yu;Xinyi Li;Guannan Wang;Li Wang
中科院分区：
文献类型：
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作者：
Yueying Wang;Myungjin Kim;Shan Yu;Xinyi Li;Guannan Wang;Li Wang
流行病模型是了解新型冠状病毒传播并最终帮助疾病预防、政策制定和资源分配的重要工具。在本文中，我们在经典的数学模型和统计模型之间建立了一个最新的接口，并提出了一个新的时空流行病模型框架来研究传染病传播的时空模式。通过惩罚样条法和加权最小二乘法，我们提出了一种拟似然方法来估计模型。所提出的估计量是相容的，并且建立了常系数的渐近正态。利用时空分析，我们提出的模型增强了流行病机制的动力学，并剖析了疾病传播的时空结构。通过一个仿真算例对该方法的数值性能进行了评估。最后，我们将所提出的方法应用于毁灭性的新冠肺炎大流行的研究。
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.