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Pangallo M, Aleta A, Del Rio-Chanona RM, Pichler A, Martín-Corral D, Chinazzi M, Lafond F, Ajelli M, Moro E, Moreno Y, Vespignani A, Farmer JD. The unequal effects of the health-economy trade-off during the COVID-19 pandemic. Nat Hum Behav 2024; 8:264-275. [PMID: 37973827 PMCID: PMC10896714 DOI: 10.1038/s41562-023-01747-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 10/05/2023] [Indexed: 11/19/2023]
Abstract
Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off.
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Affiliation(s)
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | | | | | - David Martín-Corral
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganes, Spain
| | - Matteo Chinazzi
- MOBS Lab, Northeastern University, Boston, MA, USA
- The Roux Institute, Northeastern University, Portland, ME, USA
| | - François Lafond
- Institute for New Economic Thinking at the Oxford Martin School, and Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Esteban Moro
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganes, Spain
- Connection Science, Institute for Data Science and Society, MIT, Cambridge, MA, USA
| | - Yamir Moreno
- CENTAI Institute, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
- Complexity Science Hub, Vienna, Austria
| | | | - J Doyne Farmer
- Institute for New Economic Thinking at the Oxford Martin School, and Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
- Santa Fe Institute, Santa Fe, NM, USA
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Aleta A, Martín-Corral D, Bakker MA, Pastore Y Piontti A, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini IM, Pentland A, Vespignani A, Moreno Y, Moro E. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proc Natl Acad Sci U S A 2022; 119:e2112182119. [PMID: 35696558 DOI: 10.1101/2020.12.15.20248273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023] Open
Abstract
Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.
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Affiliation(s)
| | - David Martín-Corral
- Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Zensei Technologies S.L., 28010 Madrid, Spain
| | - Michiel A Bakker
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Marco Ajelli
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405
| | - Maria Litvinova
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Natalie E Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611
| | - M Elizabeth Halloran
- Biostatistics, Bioinformatics, and Epidemiology Program, Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611
| | - Alex Pentland
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Alessandro Vespignani
- ISI Foundation, 10126 Turin, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Yamir Moreno
- ISI Foundation, 10126 Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018 Zaragoza, Spain
- Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, 50009 Zaragoza, Spain
| | - Esteban Moro
- Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Grupo Interdisciplinar de Sistemas Complejos, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
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Aleta A, Martín-Corral D, Pastore Y Piontti A, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini IM, Merler S, Pentland A, Vespignani A, Moro E, Moreno Y. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat Hum Behav 2020; 4:964-971. [PMID: 32759985 PMCID: PMC7641501 DOI: 10.1038/s41562-020-0931-9] [Citation(s) in RCA: 389] [Impact Index Per Article: 97.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/20/2020] [Indexed: 01/08/2023]
Abstract
While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2.
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Affiliation(s)
- Alberto Aleta
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - David Martín-Corral
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganés, Spain
- Zensei Technologies S.L., Madrid, Spain
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Marco Ajelli
- Bruno Kessler Foundation, Trento, Italy
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Maria Litvinova
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - M Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | | | - Alex Pentland
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, Italy.
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
| | - Esteban Moro
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganés, Spain.
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yamir Moreno
- Institute for Scientific Interchange Foundation, Turin, Italy.
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
- Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain.
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Aleta A, Martín-Corral D, Piontti APY, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini IM, Merler S, Pentland A, Vespignani A, Moro E, Moreno Y. Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic. medRxiv 2020:2020.05.06.20092841. [PMID: 32511536 PMCID: PMC7273304 DOI: 10.1101/2020.05.06.20092841] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.
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Affiliation(s)
- Alberto Aleta
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - David Martín-Corral
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganés, Spain
- Zensei Technologies S.L., Madrid, Spain
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Marco Ajelli
- Bruno Kessler Foundation, Trento Italy
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Maria Litvinova
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E. Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ira M. Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | | | - Alex Pentland
- Connection Science, Institute for Data Science and Society, MIT, Cambridge, US
| | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Esteban Moro
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganés, Spain
- Connection Science, Institute for Data Science and Society, MIT, Cambridge, US
| | - Yamir Moreno
- Institute for Scientific Interchange Foundation, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Spain
- Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Spain
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