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      Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

      research-article
      1 , 2 , , 1 , 2 , 3 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 5 , 5 , 1 , 1 , 1 , 1 , 1 , 6 , 7 , 5 , 7 , 8 , 9 , 4 , 1 , 6 , 10 , 1 , 5 , 6 , 10 , 11 , 1 , 7 , 8 , 1 , 6 , 2
      Nature Communications
      Nature Publishing Group UK
      Epidemiology, Viral epidemiology, Viral transmission, Human behaviour, Respiratory tract diseases

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          Abstract

          Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

          Abstract

          Population mobility is associated with SARS-CoV-2 transmission but its impacts on other respiratory viruses are not well understood. Here, the authors investigate associations between mobile phone-derived mobility metrics and the dynamics of 18 respiratory viruses in Seattle, Washington from 2018 to 2022.

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          Most cited references59

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          A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)

          COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries' subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.
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            Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

            It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.
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              • Record: found
              • Abstract: found
              • Article: not found

              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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                Author and article information

                Contributors
                acperof@uw.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 May 2024
                16 May 2024
                2024
                : 15
                : 4164
                Affiliations
                [1 ]GRID grid.34477.33, ISNI 0000000122986657, Brotman Baty Institute for Precision Medicine, , University of Washington, ; Seattle, WA USA
                [2 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Fogarty International Center, , National Institutes of Health, ; Bethesda, MD USA
                [3 ]PandemiX Center, Department of Science & Environment, Roskilde University, ( https://ror.org/014axpa37) Roskilde, Denmark
                [4 ]GRID grid.418309.7, ISNI 0000 0000 8990 8592, Institute for Disease Modeling, , Bill & Melinda Gates Foundation, ; Seattle, WA USA
                [5 ]Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, ( https://ror.org/007ps6h72) Seattle, WA USA
                [6 ]Department of Genome Sciences, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA USA
                [7 ]GRID grid.240741.4, ISNI 0000 0000 9026 4165, Seattle Children’s Research Institute, ; Seattle, WA USA
                [8 ]Department of Pediatrics, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA USA
                [9 ]EpiAssist LLC, Seattle, WA USA
                [10 ]Howard Hughes Medical Institute, ( https://ror.org/006w34k90) Seattle, WA USA
                [11 ]Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA USA
                Author information
                http://orcid.org/0000-0001-7341-9193
                http://orcid.org/0000-0002-4526-6772
                http://orcid.org/0000-0001-8991-4762
                http://orcid.org/0009-0005-6906-6686
                http://orcid.org/0000-0001-7882-1050
                http://orcid.org/0009-0006-2106-2173
                http://orcid.org/0009-0007-1273-879X
                http://orcid.org/0000-0001-5269-2297
                http://orcid.org/0009-0000-6257-9845
                http://orcid.org/0000-0002-1516-1865
                http://orcid.org/0000-0001-8502-9600
                http://orcid.org/0000-0003-1134-4178
                http://orcid.org/0000-0003-2870-5099
                http://orcid.org/0000-0003-3243-4711
                Article
                48528
                10.1038/s41467-024-48528-2
                11098821
                38755171
                b984af05-cb81-4f99-a2d7-1a299284f37f
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 December 2023
                : 2 May 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000011, Howard Hughes Medical Institute (HHMI);
                Funded by: FundRef https://doi.org/10.13039/100000030, U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (CDC);
                Award ID: 75D30122C14368
                Award Recipient :
                Funded by: Gates Ventures
                Categories
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                © Springer Nature Limited 2024

                Uncategorized
                epidemiology,viral epidemiology,viral transmission,human behaviour,respiratory tract diseases

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