This work examines the discrete-time networked SIR (susceptible-infected-recovered) epidemic model, where the infection and recovery parameters may be time-varying. We provide a sufficient condition for the SIR model to converge to the set of healthy states exponentially. We propose a stochastic framework to estimate the system states from observed testing data and provide an analytic expression for the error of the estimation algorithm. Employing the estimated and the true system states, we provide two novel eradication strategies that guarantee at least exponential convergence to the set of healthy states. We illustrate the results via simulations over northern Indiana, USA.

中文翻译：

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网络上SIR流行病的估计和分布式根除

这项工作研究了离散时间网络SIR（易感感染恢复）流行病模型，其中感染和恢复参数可能随时间变化。我们为SIR模型提供了足够的条件，以使其以指数形式收敛到健康状态集。我们提出了一种随机框架，用于从观察到的测试数据估计系统状态，并为估计算法的误差提供解析表达式。利用估计的状态和真实的系统状态，我们提供了两种新颖的根除策略，这些策略可确保至少以指数形式收敛到健康状态集。我们通过对美国印第安纳州北部的模拟来说明结果。