Migration rate estimation in an epidemic network

• Published in: Applied Mathematical Modelling Vol. 89; pp. 1949 - 1964
• Main Authors: Núñez-López, M, Ramos, L Alarcón, Velasco-Hernández, JX
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01-01-2021; Elsevier BV
• Subjects: COVID-19; Dengue dynamics; Epidemics; Human mobility; Markov chains; Mathematical models; Metapopulation model; Migration; Outbreaks; Parameters; Reinfection; Dengue dynamics; Reinfection; Human mobility; Migration; Metapopulation model
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2020.08.025

•We address the migration of the human population and its effect on pathogen reinfection.•We use a Markov-chain SIS metapopulation model over a network.•The contact rate is based on the infected hosts and the incidence of their neighboring locations.•We estimate from Dengue data in Mexico the dynamics of migration incorporating climate variability.
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.