Laboratory

Migration and Mobility

Project

Interrelationships between Human Mobility and Infectious Disease Dynamics

Daniela Perrotta, Jordan Klein, Egor Kotov; in Collaboration with John Palmer (Pompeu Fabra University, Barcelona, Spain), Frederic Bartumeus (Spanish National Research Council, Blanes Centre for Advanced Studies, Spain)

Detailed Description

Human mobility plays a key role in the spread of many infectious diseases. The impact of population movement on the likelihood of sustained local disease transmission is twofold. On the one hand, with the growth in the transportation infrastructure and with millions of people traveling every day, the chances of creating new routes and opportunities for disease vectors and pathogens to spread into new susceptible populations are higher than ever before. On the other hand, human mobility significantly affects the social contact and mixing patterns in the population, which in turn affects the interaction and disease transmission between susceptible and infected individuals. Having timely, accurate, and comparative data on human mobility is therefore of paramount importance for epidemic preparedness and response.

Beyond mobility alone, the spatial dynamics of infectious diseases are further shaped by environmental and socioeconomic heterogeneity, which influence population exposure and transmission risk. Modeling these dynamics therefore requires integrating human mobility with spatial variations in risk factors and, where relevant, the distribution and dynamics of transmission agents.

In this project, we investigate the potential benefits of using different types of human mobility data for outbreak prediction, mainly focusing on comparisons of the mobility patterns derived from digital traces and mobile phone activities with those derived from more traditional data sources, such as census data and mobility models. Incorporating these mobility patterns into mathematical and computational models allows us to examine the potential impact of using this type of derived human mobility data in modeling the spatial dynamics of infectious diseases. Conversely, we also use digital trace data to examine how disease outbreaks and related control policies affect human mobility and migration patterns.

Using the 2015-2016 Zika virus (ZIKV) outbreak in Colombia as a case study, we employed a stochastic metapopulation model for vector-borne disease and found that mobility networks based on mobile data more accurately captured the outbreak at national and subnational levels than official surveillance data. These results highlight the limited predictability of epidemic outbreaks in the absence of timely and high-resolution mobility data that provide a more accurate representation of human movement for infectious disease modeling (Perrotta et al, 2022).

In the context of the COVID-19 pandemic, we analyzed the impact of travel restrictions on migration, focusing on migrants from North and West Africa. Using digital trace data and linear panel models, we estimated that a destination country implementing a month-long total entry ban on arrivals from specific origin countries might have expected a reduction in migrant stock from the restricted countries compared with the counterfactual in which no travel restrictions were implemented. However, when broader societal disruptions of the pandemic were accounted for, we found that the countries implementing travel restrictions might have paradoxically experienced an increase in migrant stock. These results suggest that any reduction in inflows resulting from travel bans was more than offset by an even larger reduction in outflows, which highlights how border control policies can reshape migration dynamics in unintended ways (Klein et al, 2024).

Looking ahead, this project will focus on integrating high-resolution mobility data with environmental, socioeconomic, demographic, and behavioral factors to better capture the complexity of infectious disease dynamics. By incorporating these dimensions and adaptive human responses into modeling frameworks, we aim to advance our understanding of how mobility shapes the spatial spread of infectious diseases across different contexts.

Human mobility and the Zika virus outbreak in Colombia

comparison between ZIKV incidence (per 100,000 population) as reported by official surveillance (black dots) and different networks © Perrotta, D., Frias-Martinez, E., Piontti, A. P. y, Zhang, Q., Luengo-Oroz, M., Paolotti, D., Tizzoni, M., & Vespignani, A. (2022). Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia. PLOS Neglected Tropical Diseases, 16(7), Article 7. https://doi.org/10.1371/journal.pntd.0010565

The graph shows the comparison between ZIKV incidence (per 100,000 population) as reported by official surveillance (black dots) and as estimated from the stochastic ensemble output for each mobility network considered, namely the CDR-informed network (blue), the census network (black), the gravity network (orange), the radiation network (purple) and the radiation network calibrated to CDR-informed mobility (green).

The inset graph shows the peak week calculated from the model estimates, compared to the observed peak in the week 2016-05 (green line). While the performance of the different mobility networks is comparatively similar at the national level, the figure shows the good performance of our model, including its epidemiological assumptions, in capturing the outbreak dynamics without any fit to the observed data. More details are available in the paper.

Research Keywords:

Health Care, Public Health, Medicine, and Epidemiology, Migration

Region keywords:

Africa, Colombia, World