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Stochastic SIS Modelling: Coinfection of Two Pathogens in Two-Host Communities.

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Entropy (Basel, Switzerland)
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This study models pathogen transmission between two host classes and two pathogen types. Understanding disease extinction probabilities and outbreak dynamics is crucial for effective disease control and intervention strategies.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Stochastic Processes

Background:

  • Pathogens can infect multiple hosts, and single hosts can harbor multiple pathogens, complicating disease dynamics.
  • Zoonotic diseases like rabies exemplify pathogens with diverse host ranges.
  • Malaria and meningitis illustrate single hosts being susceptible to multiple infections.

Purpose of the Study:

  • To develop and analyze mathematical models for disease transmission dynamics involving two susceptible classes and two infectious agents.
  • To investigate the factors influencing disease extinction probabilities in a multi-host, multi-pathogen system.
  • To provide insights for effective disease control and intervention strategies.

Main Methods:

  • Formulation of stochastic models using continuous-time Markov chains (CTMC) and their deterministic counterparts.
  • Application of multi-type branching processes to analyze model statistics.
  • Calculation of two reproduction numbers and probability-generating functions for offspring.
  • Estimation of extinction probabilities using mean number of infections.

Main Results:

  • The study derived two distinct reproduction numbers, reflecting pathogen-specific transmission.
  • Probability-generating functions and mean infection numbers were utilized to estimate extinction probabilities.
  • The initial population size of infectious classes was identified as a significant factor influencing extinction probability.

Conclusions:

  • Mathematical modeling provides a framework for understanding complex disease transmission and extinction dynamics.
  • Initial infectious population size critically impacts disease extinction likelihood.
  • Insights into disease extinction and outbreak patterns can inform public health interventions and management strategies.