Most of the recent epidemic outbreaks in the world have as a trigger, a strong migratory component as has been evident in the recent Covid-19 pandemic. In this work we address the problem of migration of human populations and its effect on pathogen reinfections in the case of Dengue, using a Markov-chain susceptible-infected-susceptible (SIS) metapopulation model over a network. Our model postulates a general contact rate that represents a local measure of several factors: the population size of infected hosts that arrive at a given location as a function of total population size, the current incidence at neighboring locations, and the connectivity of the network where the disease spreads. This parameter can be interpreted as an indicator of outbreak risk at a given location. This parameter is tied to the fraction of individuals that move across boundaries (migration). To illustrate our model capabilities, we estimate from epidemic Dengue data in Mexico the dynamics of migration at a regional scale incorporating climate variability represented by an index based on precipitation data.

中文翻译：

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流行病网络中的迁移率估计


世界上最近爆发的大多数流行病都有强烈的迁徙成分作为触发因素，最近的 Covid-19 大流行就证明了这一点。在这项工作中，我们使用网络上的马尔可夫链易感者-感染者-易感者（SIS）集合种群模型来解决登革热病例中人口迁移问题及其对病原体再感染的影响。我们的模型假设了一个一般接触率，它代表了几个因素的局部测量：到达给定位置的受感染主机的人口规模，作为总人口规模的函数，邻近地点的当前发病率，以及所在位置的网络连接性。疾病传播。该参数可以解释为给定地点爆发风险的指标。该参数与跨越边界（迁移）的个体比例相关。为了说明我们的模型功能，我们根据墨西哥流行的登革热数据估计了区域范围内的移民动态，其中纳入了基于降水数据的指数所代表的气候变化。