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Discrete Models in Epidemiology: New Contagion Probability Functions Based on Real Data Behavior
Alexandra Catano-Lopez1, Daniel Rojas-Diaz2, Diana Paola Lizarralde-Bejarano2
1School of Applied Sciences and Engineering, Universidad EAFIT, Medellín, Antioquia, Colombia. acatano@eafit.edu.co.
Bulletin of Mathematical Biology
|September 22, 2022
Summary
This study introduces novel contagion probability functions to improve discrete-time disease models by overcoming population homogeneity assumptions. These enhanced models better simulate disease dynamics and public health interventions.
Area of Science:
- Epidemiology
- Mathematical Biology
- Computational Science
Background:
- Discrete-time models are valuable for analyzing disease dynamics using real-world data.
- A key limitation of current discrete models is the assumption of population homogeneity, restricting their accuracy.
- Addressing this limitation is crucial for more realistic disease transmission modeling.
Purpose of the Study:
- To develop and introduce new contagion probability functions for discrete-time epidemiological models.
- To overcome the homogeneity assumption inherent in traditional discrete models.
- To enhance the descriptive and fitting capabilities of disease dynamics models.
Main Methods:
- Proposed novel contagion probability functions based on two infection paradigms.
- Incorporated factors like infectious interaction probability, contact rates, and social connectivity.
- Integrated these functions into discrete-time models and validated with real-world data for COVID-19 and dengue.
Main Results:
- Successfully overcame the population homogeneity assumption in discrete-time models.
- Evaluated model performance using COVID-19 data from Germany and South Korea, and dengue data from Colombia.
- Described oscillatory dynamics for dengue using the proposed contagion probabilities and biologically relevant parameters.
Conclusions:
- The developed contagion probability functions significantly improve discrete-time models for disease dynamics.
- These enhanced models offer better simulation accuracy for infectious disease outbreaks and public policy interventions.
- Implementation of these probabilities can lead to more effective public health strategies.
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