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Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil.
Peixoto, Pedro S; Marcondes, Diego; Peixoto, Cláudia; Oliva, Sérgio M.
Afiliación
  • Peixoto PS; Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
  • Marcondes D; Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
  • Peixoto C; Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
  • Oliva SM; Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
PLoS One ; 15(7): e0235732, 2020.
Article en En | MEDLINE | ID: mdl-32673323
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía Viral / Infecciones por Coronavirus / Aplicaciones Móviles / Modelos Biológicos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Humans País/Región como asunto: America do sul / Brasil Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía Viral / Infecciones por Coronavirus / Aplicaciones Móviles / Modelos Biológicos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Humans País/Región como asunto: America do sul / Brasil Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos