We present a framework for discrete network-based modeling of TB epidemiology in US counties using publicly available synthetic datasets. We explore the dynamics of this modeling framework by simulating the hypothetical spread of disease over 2 years resulting from a single active infection in Washtenaw County, MI. We find that for sufficiently large transmission rates that active transmission outweighs reactivation, disease prevalence is sensitive to the contact weight assigned to transmissions between casual contacts (that is, contacts that do not share a household, workplace, school, or group quarter). Workplace and casual contacts contribute most to active disease transmission, while household, school, and group quarter contacts contribute relatively little. Stochastic features of the model result in significant uncertainty in the predicted number of infections over time, leading to challenges in model calibration and interpretation of model-based predictions. Finally, predicted infections were more localized by household location than would be expected if they were randomly distributed. This modeling framework can be refined in later work to study specific county and multi-county TB epidemics in the USA.

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使用合成数据集的基于网络的结核病动力学流行病学建模框架

我们使用公开可用的合成数据集提出了一个基于离散网络的美国县结核病流行病学建模框架。我们通过模拟密歇根州沃什特诺县单一活动性感染导致的 2 年内疾病假设传播来探索该建模框架的动态。我们发现，对于主动传播超过重新激活的足够大的传播率，疾病流行率对分配给偶然接触者（即不共享家庭、工作场所、学校或集体宿舍的接触者）之间传播的接触权重敏感。工作场所和偶然接触对主动疾病传播的贡献最大，而家庭、学校和集体宿舍接触的贡献相对较小。随着时间的推移，模​​型的随机特征导致预测的感染数量存在很大的不确定性，从而导致模型校准和基于模型的预测解释的挑战。最后，与随机分布的预期相比，预测的感染更受家庭位置的限制。这个建模框架可以在以后的工作中完善，以研究美国特定的县和多县结核病流行病。