Caused by Yersinia pestis , plague ravaged the world through three known pandemics: the First or the Justinianic (6th–8th century); the Second (beginning with the Black Death during c.1338–1353 and lasting until the 19th century); and the Third (which became global in 1894). It is debatable whether Y. pestis persisted in European wildlife reservoirs or was repeatedly introduced from outside Europe (as covered by European Union and the British Isles). Here, we analyze environmental data (soil characteristics and climate) from active Chinese plague reservoirs to assess whether such environmental conditions in Europe had ever supported “natural plague reservoirs”. We have used new statistical methods which are validated through predicting the presence of modern plague reservoirs in the western United States. We find no support for persistent natural plague reservoirs in either historical or modern Europe. Two factors make Europe unfavorable for long-term plague reservoirs: 1) Soil texture and biochemistry and 2) low rodent diversity. By comparing rodent communities in Europe with those in China and the United States, we conclude that a lack of suitable host species might be the main reason for the absence of plague reservoirs in Europe today. These findings support the hypothesis that long-term plague reservoirs did not exist in Europe and therefore question the importance of wildlife rodent species as the primary plague hosts in Europe.

Multivariate Poisson autoregressive models are common ways to fit count time series data, while the statistical inference is quite challenging. The network Poisson autoregressive model (NPAR) reduces the inference complexity by incorporating network information into the dependence structure, where the response of each individual can be explained by its lagged values and the average effect of its neighbors. However, NPAR makes one strong assumption that all individuals are homogeneous and they share a common autoregressive coefficient. Here we propose a grouped network Poisson autoregressive model (GNPAR), where the individuals are classified into different groups with group-specific parameters to describe heterogeneous nodal behaviors. We present the stationarity and ergodicity of the GNPAR model and study the asymptotic properties of the maximum likelihood estimation.We develop an EM algorithm to estimate the unknown group labels and investigate the finitesample performance of our estimation procedure using simulations. We analyze the Chicago Police Investigatory Stop Report data and find distinct dependence patterns in different neighborhoods of Chicago that could be potentially helpful for future crime prevention.

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