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A stochastic Markov-based modeling framework with demography.

Vasileios E Papageorgiou1

  • 1Department of Mathematics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece. vpapageor@math.auth.gr.

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|December 1, 2025
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Summary
This summary is machine-generated.

This study introduces novel Markov-based epidemic models accounting for changing population sizes. These models improve understanding of infectious disease severity and inform public health interventions.

Keywords:
DemographyEpidemiologyHitting timesMomentsProbability theoryStochastic models

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Statistics

Background:

  • Stochastic epidemic modeling is vital for assessing infectious disease severity.
  • Existing models often assume closed populations, neglecting demographic changes.
  • Recent attention highlights the need for dynamic, open-population models.

Purpose of the Study:

  • To present novel Markov-based epidemic models incorporating births, deaths, and migration.
  • To investigate epidemic dynamics in time-varying population sizes within a Markovian framework.
  • To develop computational methods for estimating secondary infections and hazard times.

Main Methods:

  • Developed three Markov-based epidemic models for open populations.
  • Incorporated demographic rates (births, deaths, migration) affecting transition patterns.
  • Introduced novel computational approaches for estimating stochastic features.
  • Conducted sensitivity analysis to evaluate demographic impacts.
  • Validated models using 2022 mpox outbreak data in Greece.

Main Results:

  • Demonstrated the impact of demographic dynamics on epidemic severity.
  • Showcased the first Markovian framework for epidemic models with time-varying populations.
  • Estimated secondary infections and hazard times effectively.
  • Analyzed the effect of interventions like lockdowns on disease severity.

Conclusions:

  • Demographic dynamics significantly influence epidemic outbreak severity.
  • The novel models provide a robust framework for analyzing infectious diseases in open populations.
  • Findings support health authorities in optimizing intervention strategies and timing.