Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

中文翻译：

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传染病传播的地理相关个体水平模型。

传染病模型对于了解影响疾病传播的潜在机制和预测未来疾病进展非常有用。建模已越来越多地用于评估不同控制措施的潜在影响并指导公共卫生政策决策。近年来，在开发传染病的时空模型方面取得了快速进展，最近发展的一个例子是离散时间个体水平模型（ILM）。这些模型得到了很好的发展，并为许多疾病系统的建模提供了一个通用框架；然而，他们假设两个人之间疾病传播的可能性仅取决于他们的空间距离，而不取决于他们的空间位置。在空间位置本身对于了解新发传染病的传播和确定其原因很重要的情况下，将空间位置的影响纳入模型中将是有益的。因此，在本研究中，我们将 ILM 推广到一类新的地理相关 ILM，以评估空间变化的风险因素（例如，教育、社会剥夺、环境）以及未观察到的空间结构对传染病的传播。具体来说，我们考虑使用条件自回归 (CAR) 模型来捕捉未观察到的空间结构化潜在协变量或测量误差的影响。这导致了灵活的传染病模型，可用于制定病因假设和识别异常高风险的地理区域以制定预防措施。结合模拟流行病数据和艾伯塔省季节性流感爆发数据（2009 美元），研究这些模型的可靠性。使用马尔可夫链蒙特卡罗方法将这类新模型拟合到贝叶斯统计框架内的数据。