The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible–exposed–infectious–removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of ‘superspreader’ points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19. An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. https://doi.org/10.17226/25961. Texto completo La tecnología de la información (TI) se entiende ampliamente como la tecnología habilitadora del siglo XXI. Las TIcs han transformado, y continúan transformando, todos los aspectos de nuestras vidas: el comercio…

La pandemia representa una oportunidad para agilizar la puesta en práctica de soluciones de salud digital y comprender mejor las (pre)condiciones que las favorecían, así como los mecanismos que beneficiarán su sostenibilidad después de la COVID-19.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Este año, tras décadas de avances constantes en la reducción del número de personas que viven con menos de USD 1,90 al día, la COVID-19 marcará el primer revés en la lucha contra la pobreza extrema en una generación.

A multidisciplinary journal that focuses on public health and technology, public health informatics, mass media campaigns, surveillance, and innovation in public health practice and research. Also dedicated to rapid open data sharing during epidemics.

A New Peer-Reviewed Journal with Focus on Technology, Innovation and Openess in Medical Education