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Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.

Entropy (Basel, Switzerland)·2023
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A multi-source global-local model for epidemic management.

José Ulises Márquez Urbina1,2, Graciela González Farías3, L Leticia Ramírez Ramírez3

  • 1Unidad Monterrey, CIMAT, Monterrey, N.L., México.

Plos One
|January 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for projecting the effective reproduction number (Rt) using exposure scenarios and compartmental models. This approach aids in epidemic management by estimating unobserved variables and forecasting future trends.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health Surveillance

Background:

  • The effective reproduction number (Rt) is crucial for managing epidemics.
  • Future projections of Rt can significantly enhance epidemic management strategies.

Purpose of the Study:

  • To propose a methodology for projecting Rt into the future using exposure scenarios.
  • To detail parameterization of a compartmental model for Mexico's context.
  • To estimate unobserved variables and forecast future epidemic indicators.

Main Methods:

  • Utilizing a compartmental model with adequate parametrization.
  • Developing a projection methodology based on exposure scenarios.
  • Applying the model to analyze pandemic data in a Mexican state and nationally.

Main Results:

  • The methodology successfully estimates unobserved variables like the asymptomatic population size.
  • Future active hospitalizations can be projected using defined scenarios.
  • The approach is validated through analysis of a Mexican state and a metropolitan area.

Conclusions:

  • The proposed methodology provides a robust framework for projecting Rt and supporting epidemic surveillance.
  • This approach is adaptable for various demographic levels and applicable beyond Mexico.
  • It offers solutions for developing effective surveillance systems with incomplete information.