Literature DB >> 32732433

Interdependence and the cost of uncoordinated responses to COVID-19.

David Holtz1,2, Michael Zhao1,2, Seth G Benzell2,3, Cathy Y Cao1,2, Mohammad Amin Rahimian1,4, Jeremy Yang1,2, Jennifer Allen1, Avinash Collis2,5, Alex Moehring1, Tara Sowrirajan6,7, Dipayan Ghosh1, Yunhao Zhang1, Paramveer S Dhillon2,8, Christos Nicolaides1,2,9, Dean Eckles10,2, Sinan Aral10,2.   

Abstract

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

Entities:  

Keywords:  COVID-19; geographic spillovers; peer effects; social spillovers

Mesh:

Year:  2020        PMID: 32732433      PMCID: PMC7443871          DOI: 10.1073/pnas.2009522117

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  11 in total

1.  Identifying influential and susceptible members of social networks.

Authors:  Sinan Aral; Dylan Walker
Journal:  Science       Date:  2012-06-21       Impact factor: 47.728

2.  The effect of partisanship and political advertising on close family ties.

Authors:  M Keith Chen; Ryne Rohla
Journal:  Science       Date:  2018-05-31       Impact factor: 47.728

3.  Social Connectedness: Measurement, Determinants, and Effects.

Authors:  Michael Bailey; Rachel Cao; Theresa Kuchler; Johannes Stroebel; Arlene Wong
Journal:  J Econ Perspect       Date:  2018

4.  Aggregated mobility data could help fight COVID-19.

Authors:  Caroline O Buckee; Satchit Balsari; Jennifer Chan; Mercè Crosas; Francesca Dominici; Urs Gasser; Yonatan H Grad; Bryan Grenfell; M Elizabeth Halloran; Moritz U G Kraemer; Marc Lipsitch; C Jessica E Metcalf; Lauren Ancel Meyers; T Alex Perkins; Mauricio Santillana; Samuel V Scarpino; Cecile Viboud; Amy Wesolowski; Andrew Schroeder
Journal:  Science       Date:  2020-03-23       Impact factor: 47.728

5.  Population flow drives spatio-temporal distribution of COVID-19 in China.

Authors:  Jayson S Jia; Xin Lu; Yun Yuan; Ge Xu; Jianmin Jia; Nicholas A Christakis
Journal:  Nature       Date:  2020-04-29       Impact factor: 49.962

6.  A 61-million-person experiment in social influence and political mobilization.

Authors:  Robert M Bond; Christopher J Fariss; Jason J Jones; Adam D I Kramer; Cameron Marlow; Jaime E Settle; James H Fowler
Journal:  Nature       Date:  2012-09-13       Impact factor: 49.962

7.  Exercise contagion in a global social network.

Authors:  Sinan Aral; Christos Nicolaides
Journal:  Nat Commun       Date:  2017-04-18       Impact factor: 14.919

8.  Detecting emotional contagion in massive social networks.

Authors:  Lorenzo Coviello; Yunkyu Sohn; Adam D I Kramer; Cameron Marlow; Massimo Franceschetti; Nicholas A Christakis; James H Fowler
Journal:  PLoS One       Date:  2014-03-12       Impact factor: 3.240

9.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.

Authors:  Nuria Oliver; Bruno Lepri; Harald Sterly; Renaud Lambiotte; Sébastien Deletaille; Marco De Nadai; Emmanuel Letouzé; Albert Ali Salah; Richard Benjamins; Ciro Cattuto; Vittoria Colizza; Nicolas de Cordes; Samuel P Fraiberger; Till Koebe; Sune Lehmann; Juan Murillo; Alex Pentland; Phuong N Pham; Frédéric Pivetta; Jari Saramäki; Samuel V Scarpino; Michele Tizzoni; Stefaan Verhulst; Patrick Vinck
Journal:  Sci Adv       Date:  2020-06-05       Impact factor: 14.136

10.  The effect of human mobility and control measures on the COVID-19 epidemic in China.

Authors:  Moritz U G Kraemer; Chia-Hung Yang; Bernardo Gutierrez; Chieh-Hsi Wu; Brennan Klein; David M Pigott; Louis du Plessis; Nuno R Faria; Ruoran Li; William P Hanage; John S Brownstein; Maylis Layan; Alessandro Vespignani; Huaiyu Tian; Christopher Dye; Oliver G Pybus; Samuel V Scarpino
Journal:  Science       Date:  2020-03-25       Impact factor: 47.728

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  40 in total

1.  Improving data access democratizes and diversifies science.

Authors:  Abhishek Nagaraj; Esther Shears; Mathijs de Vaan
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-08       Impact factor: 11.205

2.  Targeted maximum likelihood estimation of causal effects with interference: A simulation study.

Authors:  Paul N Zivich; Michael G Hudgens; Maurice A Brookhart; James Moody; David J Weber; Allison E Aiello
Journal:  Stat Med       Date:  2022-07-18       Impact factor: 2.497

3.  Interacting regional policies in containing a disease.

Authors:  Arun G Chandrasekhar; Paul Goldsmith-Pinkham; Matthew O Jackson; Samuel Thau
Journal:  Proc Natl Acad Sci U S A       Date:  2021-05-11       Impact factor: 11.205

4.  Synchronized nonpharmaceutical interventions for the control of COVID-19.

Authors:  Bing Zhang; Shiwen Liang; Gang Wang; Chi Zhang; Cai Chen; Min Zou; Wei Shen; Haoyu Long; Daihai He; Yuelong Shu; Xiangjun Du
Journal:  Nonlinear Dyn       Date:  2021-05-21       Impact factor: 5.022

5.  Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

Authors:  Cornelius Fritz; Emilio Dorigatti; David Rügamer
Journal:  Sci Rep       Date:  2022-03-10       Impact factor: 4.379

6.  Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries.

Authors:  Xiaoyi Han; Yilan Xu; Linlin Fan; Yi Huang; Minhong Xu; Song Gao
Journal:  Proc Natl Acad Sci U S A       Date:  2021-08-03       Impact factor: 11.205

7.  Unraveling the dynamic importance of county-level features in trajectory of COVID-19.

Authors:  Qingchun Li; Yang Yang; Wanqiu Wang; Sanghyeon Lee; Xin Xiao; Xinyu Gao; Bora Oztekin; Chao Fan; Ali Mostafavi
Journal:  Sci Rep       Date:  2021-06-22       Impact factor: 4.379

Review 8.  Transmission of Vaccination Attitudes and Uptake Based on Social Contagion Theory: A Scoping Review.

Authors:  Pinelopi Konstantinou; Katerina Georgiou; Navin Kumar; Maria Kyprianidou; Christos Nicolaides; Maria Karekla; Angelos P Kassianos
Journal:  Vaccines (Basel)       Date:  2021-06-05

9.  Association between COVID-19 outcomes and mask mandates, adherence, and attitudes.

Authors:  Dhaval Adjodah; Karthik Dinakar; Matteo Chinazzi; Samuel P Fraiberger; Alex Pentland; Samantha Bates; Kyle Staller; Alessandro Vespignani; Deepak L Bhatt
Journal:  PLoS One       Date:  2021-06-23       Impact factor: 3.240

10.  Dynamics of behavior change in the COVID world.

Authors:  Cristina Moya; Patricio Cruz Y Celis Peniche; Michelle A Kline; Paul E Smaldino
Journal:  Am J Hum Biol       Date:  2020-08-23       Impact factor: 2.947

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