Explorations of Epidemiological Dynamics across Multiple Population Hubs

TL;DR

This paper enhances epidemic modeling by integrating migration, cures, and local interventions into a network-based SIR framework, providing insights into disease spread and control strategies across interconnected populations.

Contribution

It introduces a networked SIR model with migration and cures, offering theoretical convergence results and numerical analysis of intervention timing effects.

Findings

Migration influences disease spread patterns.

Timing of cures impacts mortality rates.

Localized interventions can significantly alter epidemic dynamics.

Abstract

Understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions. We extend the classical SIR model by incorporating additional complexities such as the introduction of a cure and migration between cities. Our framework leverages a system of differential equations to simulate disease transmission across a network of interconnected cities, capturing more realistic patterns. We present theoretical results on the convergence of population sizes in the migration framework (in the absence of deaths). We also run numerical simulations to understand how the timing of the introduction of the cure affects mortality rates. Our numerical results explain how localized interventions affect the spread of the disease across cities. In summary, this work advances the modeling of epidemics to a more local scope, offering a more expressive tool for epidemiological research and public health planning.