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An Epidemic Model with Pro and Anti-vaccine Groups.

G S Harari1, L H A Monteiro2,3

  • 1Universidade Presbiteriana Mackenzie, PPGEEC, São Paulo, SP, Brazil.

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|July 8, 2022
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Summary

This study models how misinformation affects vaccination. Low vaccination rates and lack of pro-vaccine information similarly increase long-term infections, impacting public health strategies.

Keywords:
COVID-19Epidemic modelFake newsVaccination

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Vaccination is crucial for disease control, but public perception is influenced by information.
  • Misinformation and disinformation campaigns can sway individuals' vaccination decisions.
  • Understanding population dynamics in response to vaccine information is vital for effective public health interventions.

Purpose of the Study:

  • To develop and analyze an epidemiological model incorporating pro- and anti-vaccination groups.
  • To investigate the impact of information (true and false news) on vaccine acceptance and disease spread.
  • To derive analytical expressions for key epidemiological parameters and steady states.

Main Methods:

  • Formulation of a mathematical model using three ordinary differential equations.
  • Derivation of analytical expressions for the disease-free steady state, endemic steady state, and basic reproduction number.
  • Numerical simulations using COVID-19 pandemic parameters to validate analytical findings.

Main Results:

  • Analytical demonstration that low vaccination rates and no influx into the pro-vaccination group yield similar long-term infection levels.
  • The model quantifies the influence of information dynamics on disease transmission.
  • Simulations illustrate the theoretical results in a real-world pandemic context.

Conclusions:

  • Information dynamics significantly influence vaccination uptake and disease prevalence.
  • Public health strategies must address both vaccination rates and the flow of information to combat infectious diseases.
  • The model provides insights into managing vaccine hesitancy and its epidemiological consequences.