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10.1016/j.apm.2020.08.025 http://scihub22266oqcxt.onion/10.1016/j.apm.2020.08.025 32952269!7486824!32952269     free     free     free       Appl+Math+Model 2021 ; 89 (ä): 1949-1964 Nephropedia Template TP {{tp|p=32952269|t=2021. Migration rate estimation in an epidemic network |pdf=|usr=}}{{32952269}} gab.com Text Migration rate estimation in an epidemic network JO: Appl Math Model http://www.kidney.de/pget.php?&tw=1&pmid=32952269 #FOAvip 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. Twit Text FOAVip Migration rate estimation in an epidemic network ????Appl Math Model http://www.kidney.de/pget.php?&tw=1&pmid=32952269 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32952269 #FOAvip English Wikipedia <ref>{{Cite pmid|32952269}}</ref> Migration rate estimation in an epidemic network #MMPMID32952269 Nunez-Lopez M; Alarcon Ramos L; Velasco-Hernandez JX Appl Math Model 2021[Jan]; 89 (ä): 1949-1964 PMID32952269show ga 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. äMigration rate estimation in an epidemic network (epmc).pdf DeepDyve Pubget Overpricing