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Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil

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  • Pedro S Peixoto
  • Diego Marcondes
  • Cláudia Peixoto
  • Sérgio M Oliva

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.

Suggested Citation

  • Pedro S Peixoto & Diego Marcondes & Cláudia Peixoto & Sérgio M Oliva, 2020. "Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0235732
    DOI: 10.1371/journal.pone.0235732
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    References listed on IDEAS

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    1. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling

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    Cited by:

    1. Arindam Ray & Wolfgang Jank & Kaushik Dutta & Matthew Mullarkey, 2023. "An LSTM + Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 440-457, March.
    2. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    3. Timo Mitze & Reinhold Kosfeld, 2022. "The propagation effect of commuting to work in the spatial transmission of COVID-19," Journal of Geographical Systems, Springer, vol. 24(1), pages 5-31, January.
    4. Hamza Zubair & Ampol Karoonsoontawong & Kunnawee Kanitpong, 2022. "Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area," Sustainability, MDPI, vol. 14(15), pages 1-26, July.
    5. María Hierro & Adolfo Maza, 2023. "Spatial contagion during the first wave of the COVID‐19 pandemic: Some lessons from the case of Madrid, Spain," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 474-492, April.
    6. Francesc Aràndiga & Antonio Baeza & Isabel Cordero-Carrión & Rosa Donat & M. Carmen Martí & Pep Mulet & Dionisio F. Yáñez, 2020. "A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    7. M. R. Martines & R. V. Ferreira & R. H. Toppa & L. M. Assunção & M. R. Desjardins & E. M. Delmelle, 2021. "Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities," Journal of Geographical Systems, Springer, vol. 23(1), pages 7-36, January.
    8. Izabela Sobiech Pellegrini, 2022. "Untimely Reopening? Increase in the Number of New COVID‐19 Cases After Reopening in One Brazilian State," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 675-693, August.

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