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Estimating Virus Production Rates in Aquatic Systems
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Migration rate estimation in an epidemic network.

M Núñez-López1, L Alarcón Ramos2, J X Velasco-Hernández3

  • 1Department of Mathematics, ITAM Río Hondo 1, Ciudad de México 01080, México.

Applied Mathematical Modelling
|September 21, 2020
PubMed
Summary

Human migration drives epidemic outbreaks like Dengue. This study uses a metapopulation model to analyze how population movement and climate variability influence disease spread and reinfection risk.

Keywords:
Dengue dynamicsHuman mobilityMetapopulation modelMigrationReinfection

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Population Dynamics

Background:

  • Recent epidemic outbreaks, including COVID-19, are strongly linked to human migration.
  • Understanding the role of population movement is crucial for predicting and controlling infectious disease spread.

Purpose of the Study:

  • To investigate the impact of human population migration on pathogen reinfection dynamics.
  • To develop and apply a metapopulation model for analyzing Dengue transmission patterns.

Main Methods:

  • Utilized a susceptible-infected-susceptible (SIS) Markov-chain metapopulation model on a network.
  • Defined a general contact rate incorporating local factors, including arriving infected hosts and network connectivity.
  • Estimated regional migration dynamics in Mexico using Dengue epidemic data and precipitation-based climate variability.

Main Results:

  • The model's contact rate serves as an indicator of local outbreak risk, directly influenced by migration.
  • Migration dynamics were successfully estimated at a regional scale, considering climate variability.
  • The study integrates climate data (precipitation) into migration pattern analysis for Dengue.

Conclusions:

  • Human migration is a significant factor in the reinfection dynamics of diseases like Dengue.
  • The developed metapopulation model provides a framework for assessing outbreak risk influenced by migration and climate.
  • Regional-scale migration patterns can be inferred by combining epidemic data with climate variability.