5
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Addressing the socioeconomic divide in computational modeling for infectious diseases

      brief-report

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics, yet these concepts are often at the margins of the computational modeling community. Building on recent research studies in the area of digital and computational epidemiology, we provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

          Abstract

          The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

          Related collections

          Most cited references63

          • Record: found
          • Abstract: found
          • Article: not found

          The COVID-19 pandemic and health inequalities

          This essay examines the implications of the COVID-19 pandemic for health inequalities. It outlines historical and contemporary evidence of inequalities in pandemics—drawing on international research into the Spanish influenza pandemic of 1918, the H1N1 outbreak of 2009 and the emerging international estimates of socio-economic, ethnic and geographical inequalities in COVID-19 infection and mortality rates. It then examines how these inequalities in COVID-19 are related to existing inequalities in chronic diseases and the social determinants of health, arguing that we are experiencing a syndemic pandemic. It then explores the potential consequences for health inequalities of the lockdown measures implemented internationally as a response to the COVID-19 pandemic, focusing on the likely unequal impacts of the economic crisis. The essay concludes by reflecting on the longer-term public health policy responses needed to ensure that the COVID-19 pandemic does not increase health inequalities for future generations.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Closing the gap in a generation: health equity through action on the social determinants of health.

            The Commission on Social Determinants of Health, created to marshal the evidence on what can be done to promote health equity and to foster a global movement to achieve it, is a global collaboration of policy makers, researchers, and civil society, led by commissioners with a unique blend of political, academic, and advocacy experience. The focus of attention is on countries at all levels of income and development. The commission launched its final report on August 28, 2008. This paper summarises the key findings and recommendations; the full list is in the final report.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Mobility network models of COVID-19 explain inequities and inform reopening

              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.
                Bookmark

                Author and article information

                Contributors
                michele.tizzoni@isi.it
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 May 2022
                24 May 2022
                2022
                : 13
                : 2897
                Affiliations
                [1 ]GRID grid.418750.f, ISNI 0000 0004 1759 3658, ISI Foundation, ; Turin, Italy
                [2 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Global Health, School of Public Health, , Boston University, ; Boston, MA USA
                [3 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Center for Antiracist Research, , Boston University, ; Boston, MA USA
                [4 ]GRID grid.5146.6, ISNI 0000 0001 2149 6445, Department of Network and Data Science, , Central European University, ; 1100 Vienna, Austria
                [5 ]GRID grid.423969.3, ISNI 0000 0001 0669 0135, Alfréd Rényi Institute of Mathematics, ; 1053 Budapest, Hungary
                [6 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, School of Mathematical Sciences, , Queen Mary University of London, ; London, UK
                [7 ]GRID grid.213910.8, ISNI 0000 0001 1955 1644, Department of Biology, , Georgetown University, ; Washington, DC USA
                Author information
                http://orcid.org/0000-0001-7246-2341
                http://orcid.org/0000-0001-9170-8714
                http://orcid.org/0000-0001-5382-8950
                http://orcid.org/0000-0002-5559-3064
                Article
                30688
                10.1038/s41467-022-30688-8
                9130127
                35610237
                58e6e63f-c92b-4f12-9a7e-e32d36339f6d
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 February 2022
                : 13 May 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100007364, Fondazione CRT (CRT Foundation);
                Funded by: FundRef https://doi.org/10.13039/100000877, Rockefeller Foundation;
                Award ID: 2020 EEO 026
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: H2020-871042
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: R01GM123007
                Award Recipient :
                Categories
                Comment
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                infectious diseases,computational models
                Uncategorized
                infectious diseases, computational models

                Comments

                Comment on this article