Modeling Communicable Diseases, Human Mobility, and Epidemics: A Review

Abstract

The spatiotemporal propagation patterns of recent infectious diseases, originated as localized epidemic outbreaks and eventually becoming global pandemics, are highly influenced by human mobility. Case exportation from endemic areas to the rest of the countries has become unavoidable because of the striking growth of the global mobility network, helping to overcome the physical distance existing between faraway regions. In this context, understanding the features driving contagions upon the arrival of an index case in local environments constitutes an essential task to devise policies aimed at avoiding the community transmission of these diseases and the subsequent case exportation to other unaffected areas. In this review, an overview of the different models addressing this topic is given, focusing on the movement-interaction-return model and different subsequent frameworks introduced to explain the complex interplay between the recurrent movements and contagion dynamics.

Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico

“…In fact, [25] posits that the spread of an epidemic largely depends on the likelihood of infection and individual human interaction with mobility networks playing a vital role in aiding temporal and spatial dynamics of disease evolution within human population. This observation was further affirmed by [49] who compared the evolution of an infectious disease in a local and global scale to a reaction-diffusion process with the reaction stage corresponding to the interaction of the hosts of the pathogen with the susceptible group and the diffusion stage corresponding to the movement or mobility of the infected group, spatially propagating the pathogen.…”

Long- and short-term effects of cross-immunity in epidemic dynamics

“…In this approach, cross-immunity should be indispensable, as the interactions between SARS-CoV-2 variants illustrate. (ii) To gather data at a microscopic level, registering sub-regional incidences, capturing population movements across road or airplane networks [14, 18, 36, 37], or quantifying the structure of social contact networks (often depending on age and cultural aspects of a given group) [38]. Regarding prediction, these details might work similarly to how abundant, localized data alleviates the chaotic weather dynamics—but gathering such low-level data in human behavior has a distinct ethic dimension that makes it problematic.…”

“…The SARS-CoV-2 pandemic also exposed the limitations of compartmental models to make helpful predictions [10]. Numerous alternatives are being developed to mitigate these shortages [11][12][13][14][15][16][17][18]. Several models have incorporated cross-immunity as well [5,[19][20][21], often mixed with additional ingredients (e.g.…”

Effects of human mobility on the spread of Dengue in the region of Caldas, Colombia

“…In this context, individuals reside in specific patches but have the freedom to move to other destinations, such as workplaces. In the following we adopt a continous-time version of the framework introduced by Soriano-Paños et al [38,48,49] as it allows to capture the recurrent nature of human commuting flows. We assume that each area i is characterized by its number of residents N i h and its number of mosquitoes N i m .…”