Propensity matrix method for age dependent stochastic infectious disease models

TL;DR

This paper introduces the propensity matrix update graph method, a flexible and efficient approach for implementing age-structured stochastic epidemic models, aiding quick modeling during emerging infectious disease outbreaks.

Contribution

The paper presents a novel propensity matrix update graph method for efficient implementation of age-dependent stochastic infectious disease models.

Findings

Efficient implementation of age-structured stochastic models.

Code base available for epidemic forecasting.

Method enhances flexibility and speed in modeling.

Abstract

Mathematical modeling is one of the key factors of the effective control of newly found infectious diseases, such as COVID-19. Our knowledge about the parameters and the course of the infection is highly limited in the beginning of the epidemic, hence computer implementation of the models have to be quick and flexible. The propensity matrix - update graph method we discuss in this paper serves as a convenient approach to efficiently implement age structured stochastic epidemic models. The code base we implemented for our forecasting work is also included in the attached GitHub repository.