ArticlePloS one2020
Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.Royal Society open science · 2025Article
- Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan.Scientific reports · 2023Article
- The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.PNAS nexus · 2023Article
- Scale-free dynamics of COVID-19 in a Brazilian city.Applied mathematical modelling · 2023Article
- Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data.Scientific reports · 2023Article
- Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income.PLOS global public health · 2023Article
- Impact of network centrality and income on slowing infection spread after outbreaks.Applied network science · 2023Article
- Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities.Transportation research interdisciplinary perspectives · 2023Article
- On the role of financial support programs in mitigating the SARS-CoV-2 spread in Brazil.BMC public health · 2022Article
- Big data from a popular app reveals that fishing creates superhighways for aquatic invaders.PNAS nexus · 2022Article
- Spatial contagion during the first wave of the COVID-19 pandemic: Some lessons from the case of Madrid, Spain.Regional science policy & practice · 2022Article
- Multiscale heterogeneous optimal lockdown control for COVID-19 using geographic information.Scientific reports · 2022Article
- Changes in the use of mobile devices during the crisis: Immediate response to the COVID-19 pandemic.Computers in human behavior reports · 2022Article
- Pandemic-influenced human mobility on tribal lands in California: Data sparsity and analytical precision.PloS one · 2022Article
- The propagation effect of commuting to work in the spatial transmission of COVID-19.Journal of geographical systems · 2022Article
- JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook.Journal of urban economics · 2022Article
- Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network.Journal of complex networks · 2021Article
- A multi-scale agent-based model of infectious disease transmission to assess the impact of vaccination and non-pharmaceutical interventions: The COVID-19 case.Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen) · 2021Article
- A snapshot of a pandemic: The interplay between social isolation and COVID-19 dynamics in Brazil.Patterns (New York, N.Y.) · 2021Article
- The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data.Applied geography (Sevenoaks, England) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. This study focuses on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high-risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk. The mathematical framework developed in this paper is quite general and may be applied to locations around the world to evaluate the risk of infection by diseases, in special the COVID-19, when geolocation data is available.
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