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Effective population size in simple infectious disease models.

Madi Yerlanov¹, Piyush Agarwal¹, Caroline Colijn¹

¹Department of Mathematics, Simon Fraser University, Burnaby, Canada.

Journal of Mathematical Biology

|November 5, 2023

Summary

This study introduces effective population size for infectious disease outbreak modeling, simplifying complex analyses. Simple SIR models using this concept accurately fit real-world COVID-19 data, even with population heterogeneities.

Keywords:

COVID-19 Compartmental model Effective population size Infectious disease modelling Population structure SIR

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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Published on: July 4, 2007

Area of Science:

Epidemiology
Mathematical Modeling
Infectious Disease Dynamics

Background:

Traditional infectious disease models rely on census population size, which may not reflect the exposed population due to structures or controls.
Incorporating population complexities into models increases fitting challenges, especially when details are unknown.

Purpose of the Study:

To introduce and evaluate the concept of effective population size as a simplified alternative in infectious disease outbreak modeling.
To assess the utility of effective population size in standard compartmental models, such as the SIR model.

Main Methods:

Defined effective population size as the population segment actively involved in an outbreak.
Conducted simulation studies to test the model's performance.
Applied the model to real-world COVID-19 outbreak data from China.

Main Results:

Simple SIR models incorporating effective population size demonstrated a good fit to outbreak data.
This approach proved effective even for datasets exhibiting complexities beyond simple SIR dynamics.

Conclusions:

Effective population size offers a valuable simplification for infectious disease outbreak modeling, reducing complexity without sacrificing accuracy.
The concept, though established in genetics, shows significant promise for epidemiological applications, particularly for diseases like COVID-19.