123 research outputs found

    Emerging viral infections in vulnerable populations:Epidemiology and mathematical modeling

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    Emerging viral infections in vulnerable populations:Epidemiology and mathematical modeling

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    Consistency of community detection in networks under degree-corrected stochastic block models

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    Community detection is a fundamental problem in network analysis, with applications in many diverse areas. The stochastic block model is a common tool for model-based community detection, and asymptotic tools for checking consistency of community detection under the block model have been recently developed. However, the block model is limited by its assumption that all nodes within a community are stochastically equivalent, and provides a poor fit to networks with hubs or highly varying node degrees within communities, which are common in practice. The degree-corrected stochastic block model was proposed to address this shortcoming and allows variation in node degrees within a community while preserving the overall block community structure. In this paper we establish general theory for checking consistency of community detection under the degree-corrected stochastic block model and compare several community detection criteria under both the standard and the degree-corrected models. We show which criteria are consistent under which models and constraints, as well as compare their relative performance in practice. We find that methods based on the degree-corrected block model, which includes the standard block model as a special case, are consistent under a wider class of models and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not. On the other hand, in practice, the degree correction involves estimating many more parameters, and empirically we find it is only worth doing if the node degrees within communities are indeed highly variable. We illustrate the methods on simulated networks and on a network of political blogs.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1036 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). With Correction

    Estimating the burden and modeling mitigation strategies of pork-related hepatitis E virus foodborne transmission in representative European countries

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    Hepatitis E virus (HEV) is an emerging zoonotic pathogen posing global health burden, and the concerns in Europe are tremendously growing. Pigs serve as a main reservoir, contributing to pork-related foodborne transmission. In this study, we aim to specifically simulate this foodborne transmission route and to assess potential interventions. We firstly established a dose-response relationship between the risk of transmission to human and the amount of ingested viruses. We further estimated the incidence of HEV infection specifically attributed to pork-related foodborne transmission in four representative European countries. Finally, we demonstrated a proof-of-concept of mitigating HEV transmission by implementing vaccination in human and pig populations. Our modeling approach bears essential implications for better understanding the transmission of pork-related foodborne HEV and for developing mitigation strategies
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