Impact of Distance on Epidemiological Dynamics in Human Connection Network with Mobility

TL;DR

This paper explores how the physical distance between individuals affects the spread of infectious diseases during human mobility, providing mathematical models that align with COVID-19 transmission data.

Contribution

It introduces a novel focus on the impact of distance on epidemiological parameters during human movement, extending traditional metapopulation models.

Findings

Distance significantly influences $R_0$ and $eta_{critical}$ values.

Model closely matches observed COVID-19 spread patterns.

Provides mathematical expressions linking distance to transmission metrics.

Abstract

The spread of infectious diseases is often influenced by human mobility across different geographical regions. Although numerous studies have investigated how diseases like SARS and COVID-19 spread from China to various global locations, there remains a gap in understanding how the movement of individuals contributes to disease transmission on a more personal or human-to-human level. Typically, researchers have employed the concept of metapopulation movement to analyze how diseases move from one location to another. This paper shifts focus to the dynamics of disease transmission, incorporating the critical factor of distance between an infected person and a healthy individual during human movement. The study delves into the impact of distance on various parameters of epidemiological dynamics throughout human mobility. Mathematical expressions for important epidemiological metrics, such as the basic reproduction number ($R_0$) and the critical infection rate ($\beta_{critical}$), are derived in relation to the distance between individuals. The results indicate that the proposed model closely aligns with observed patterns of COVID-19 spread based on the analysis done on the available datasets.