An Exploration of Modeling Approaches for Capturing Seasonal Transmission in Stochastic Epidemic Models

TL;DR

This paper reviews various modeling approaches for incorporating seasonal transmission patterns into stochastic epidemic models, emphasizing their importance for accurate disease prediction and control strategies.

Contribution

It systematically compares different methods for modeling seasonality in infectious diseases and demonstrates their application within a stochastic SIR framework.

Findings

Seasonality significantly affects disease transmission dynamics.

Different modeling approaches capture seasonal effects with varying complexity.

Inclusion of seasonality improves epidemic prediction accuracy.

Abstract

Seasonal variations in the incidence of infectious diseases are a well-established phenomenon, driven by factors such as climate changes, social behaviors, and ecological interactions that influence host susceptibility and transmission rates. While seasonality plays a significant role in shaping epidemiological dynamics, it is often overlooked in both empirical and theoretical studies. Incorporating seasonal parameters into mathematical models of infectious diseases is crucial for accurately capturing disease dynamics, enhancing the predictive power of these models, and developing successful control strategies. This paper highlights key modeling approaches for incorporating seasonality into disease transmission, including sinusoidal functions, periodic piecewise linear functions, Fourier series expansions, Gaussian functions, and data-driven methods, accompanied by real-world examples. Additionally, a stochastic Susceptible-Infected-Recovered (SIR) model with seasonal transmission is demonstrated through numerical simulations. Important outcome measures, such as the basic and instantaneous reproduction numbers and the probability of a disease outbreak using branching process approximation of the Markov chain, are also presented to illustrate the impact of seasonality on disease dynamics.