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A multi-type branching process model for epidemics with application to COVID-19.

Arnab Kumar Laha1, Sourav Majumdar1

  • 1Indian Institute of Management Ahmedabad, Ahmedabad, India.

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This study models infectious disease epidemics using a Multi-type Branching Process, incorporating real-world factors like citizen behavior and government interventions. The model accurately fits COVID-19 data and forecasts future cases, aiding health policy planning.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Infectious disease epidemics pose significant public health challenges.
  • Accurate modeling is crucial for understanding disease spread and impact.
  • Existing models may not fully capture dynamic factors influencing epidemics.

Purpose of the Study:

  • To develop and apply a flexible mathematical model for infectious disease epidemics.
  • To incorporate time-varying parameters reflecting societal and viral factors.
  • To estimate key epidemic quantities and forecast disease trajectories.

Main Methods:

  • Utilized a Multi-type Branching Process framework.
  • Incorporated non-identical Poisson distributions with time-varying parameters.
  • Fitted the model to COVID-19 caseload data from India, South Korea, UK, and US.

Main Results:

  • The model demonstrated a good fit to historical COVID-19 data.
  • The model provided accurate short-term forecasts for caseloads in studied countries.
  • The model successfully estimated unobserved quantities like undetected cases.

Conclusions:

  • The developed Multi-type Branching Process model is effective for epidemic analysis.
  • The model's flexibility allows for incorporating diverse real-world epidemic drivers.
  • This approach offers valuable insights for public health policy and intervention strategies.