Spatially Informed Back-Calculation for Spatio-Temporal Infectious Disease Models

Gyanendra Pokharel, R. Deardon
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引用次数: 1

Abstract

Abstract In epidemiological studies, the complete history of the disease system is seldom available; for example, we rarely observe the infection times of individuals but rather dates of diagnosis/disease reporting. The method of back-calculation together with prior knowledge about the distribution of the time from the infection to the disease reporting, called the incubation period, can be used to estimate unobserved infection times. Here, we consider the use of back-calculation in the context of spatial infectious disease models, extending the method to incorporate spatial information in the back-calculation method itself. Such a method should improve the quality of the fitted model, allowing us to better identify characteristics of the disease system of interest. We show that it is possible to better infer the underlying disease dynamics via the method of spatial back-calculation.
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时空传染病模型的空间信息反演
在流行病学研究中,很少有完整的疾病系统史;例如,我们很少观察个人的感染时间,而是观察诊断/疾病报告的日期。反向计算的方法与有关从感染到疾病报告的时间分布的先验知识,称为潜伏期,可以用来估计未观察到的感染时间。在这里,我们考虑在空间传染病模型的背景下使用反计算,扩展该方法,将空间信息纳入反计算方法本身。这种方法可以提高拟合模型的质量,使我们能够更好地识别感兴趣的疾病系统的特征。我们表明,通过空间反算的方法可以更好地推断潜在的疾病动力学。
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