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Arija Prieto P, Antonini M, Ammi M, Genie M, Paolucci F. Political determinants of COVID-19 restrictions and vaccine rollouts: The case of regional elections in Italy and Spain. Health Policy 2024; 145:105082. [PMID: 38781708 DOI: 10.1016/j.healthpol.2024.105082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/15/2024] [Accepted: 05/12/2024] [Indexed: 05/25/2024]
Abstract
The COVID-19 pandemic is one of the most significant public health crises in modern history, with considerable impacts on the policy frameworks of national governments. In response to the pandemic, non-pharmaceutical interventions (NPIs) and mass vaccination campaigns have been employed to protect vulnerable groups. Through the lens of Political Budget Cycle (PBC) theory, this study explores the interplay between incumbent electoral concerns and political dynamics in influencing the implementation of NPIs and vaccination rollout within the administrative regions of Italy and Spain during the period spanning June 2020 to July 2021. The results reveal that incumbents up for the next scheduled election are 5.8 % more likely to increase the stringency of containment measures than those that face a term limit. The findings also demonstrate that the seats of the incumbent and coalition parties in parliament and the number of parties in the coalition have a negative effect on both the efficiency of the vaccination rollout and the stringency of NPIs. Additionally, the competitiveness of the election emerges as an important predictor of the strictness of NPIs. Therefore, our results suggest that incumbents may strategically manipulate COVID-19 policy measures to optimize electoral outcomes. The study underscores the substantive influence of political incentives, competitive electoral environments, and government coalitions on policy formulation during health emergencies.
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Affiliation(s)
- Pablo Arija Prieto
- Department of Sociology and Business Law, University of Bologna, Bologna 40126, Italy
| | - Marcello Antonini
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia; Department of Health Policy, London School of Economics and Political Science, London, WC2A 2AE, UK.
| | - Mehdi Ammi
- School of Public Policy and Administration, Carleton University, Richcraft Hall, 1125 Colonel By Dr, Ottawa, ON, K1S 5B6, Canada
| | - Mesfin Genie
- Newcastle Business School, University of Newcastle, Newcastle, NSW, 2300, Australia; Health Economics Research Unit, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK; Department of Population Health Sciences, Duke University, 215 Morris Street, Durham, NC, 27701, USA
| | - Francesco Paolucci
- Department of Sociology and Business Law, University of Bologna, Bologna 40126, Italy; Newcastle Business School, University of Newcastle, Newcastle, NSW, 2300, Australia
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Martell M, Terry N, Sengupta R, Salazar C, Errett NA, Miles SB, Wartman J, Choe Y. Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic. PLoS One 2024; 19:e0303180. [PMID: 38728283 PMCID: PMC11086835 DOI: 10.1371/journal.pone.0303180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/20/2024] [Indexed: 05/12/2024] Open
Abstract
Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.
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Affiliation(s)
- Matthew Martell
- Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America
| | - Nick Terry
- Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America
| | - Ribhu Sengupta
- Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America
| | - Chris Salazar
- Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America
| | - Nicole A. Errett
- Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
| | - Scott B. Miles
- Human Centered Design & Engineering, University of Washington, Seattle, WA, United States of America
| | - Joseph Wartman
- Civil & Environmental Engineering, University of Washington, Seattle, WA, United States of America
| | - Youngjun Choe
- Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America
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Ahmed F, Shafer L, Malla P, Hopkins R, Moreland S, Zviedrite N, Uzicanin A. Systematic review of empiric studies on lockdowns, workplace closures, and other non-pharmaceutical interventions in non-healthcare workplaces during the initial year of the COVID-19 pandemic: benefits and selected unintended consequences. BMC Public Health 2024; 24:884. [PMID: 38519891 PMCID: PMC10960383 DOI: 10.1186/s12889-024-18377-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 03/17/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND We conducted a systematic review aimed to evaluate the effects of non-pharmaceutical interventions within non-healthcare workplaces and community-level workplace closures and lockdowns on COVID-19 morbidity and mortality, selected mental disorders, and employment outcomes in workers or the general population. METHODS The inclusion criteria included randomized controlled trials and non-randomized studies of interventions. The exclusion criteria included modeling studies. Electronic searches were conducted using MEDLINE, Embase, and other databases from January 1, 2020, through May 11, 2021. Risk of bias was assessed using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analysis and sign tests were performed. RESULTS A total of 60 observational studies met the inclusion criteria. There were 40 studies on COVID-19 outcomes, 15 on anxiety and depression symptoms, and five on unemployment and labor force participation. There was a paucity of studies on physical distancing, physical barriers, and symptom and temperature screening within workplaces. The sign test indicated that lockdown reduced COVID-19 incidence or case growth rate (23 studies, p < 0.001), reproduction number (11 studies, p < 0.001), and COVID-19 mortality or death growth rate (seven studies, p < 0.05) in the general population. Lockdown did not have any effect on anxiety symptoms (pooled standardized mean difference = -0.02, 95% CI: -0.06, 0.02). Lockdown had a small effect on increasing depression symptoms (pooled standardized mean difference = 0.16, 95% CI: 0.10, 0.21), but publication bias could account for the observed effect. Lockdown increased unemployment (pooled mean difference = 4.48 percentage points, 95% CI: 1.79, 7.17) and decreased labor force participation (pooled mean difference = -2.46 percentage points, 95% CI: -3.16, -1.77). The risk of bias for most of the studies on COVID-19 or employment outcomes was moderate or serious. The risk of bias for the studies on anxiety or depression symptoms was serious or critical. CONCLUSIONS Empiric studies indicated that lockdown reduced the impact of COVID-19, but that it had notable unwanted effects. There is a pronounced paucity of studies on the effect of interventions within still-open workplaces. It is important for countries that implement lockdown in future pandemics to consider strategies to mitigate these unintended consequences. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration # CRD42020182660.
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Affiliation(s)
- Faruque Ahmed
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA.
| | - Livvy Shafer
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Pallavi Malla
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Roderick Hopkins
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
- Cherokee Nation Operational Solutions, Tulsa, OK, USA
| | - Sarah Moreland
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Nicole Zviedrite
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
| | - Amra Uzicanin
- Division of Global Migration Health, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop V18-2, Atlanta, GA, 30329-4027, USA
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Wu G, Zhang W, Wu W, Wang P, Huang Z, Wu Y, Li J, Zhang W, Du Z, Hao Y. Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States. Front Public Health 2024; 12:1343950. [PMID: 38450145 PMCID: PMC10915018 DOI: 10.3389/fpubh.2024.1343950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Introduction Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics. Methods Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic. Results We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022. Conclusion Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.
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Affiliation(s)
- Gonghua Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wanfang Zhang
- Guangzhou Liwan District Center for Disease Prevention and Control, Guangzhou, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Pengyu Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zitong Huang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yueqian Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Junxi Li
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
- Guangzhou Joint Research Center for Disease Surveillance and Risk Assessment, Sun Yat-sen University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
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5
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Mello MM, Jiang D, Platt E, Moran-McCabe K, Burris S. Legal infrastructure for pandemic response: lessons not learnt in the US. BMJ 2024; 384:e076269. [PMID: 38346813 DOI: 10.1136/bmj-2023-076269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Affiliation(s)
- Michelle M Mello
- Stanford Law School, Stanford, CA, USA
- Department of Health Policy, Stanford University School of Medicine, Stanford, USA
| | | | - Elizabeth Platt
- Center for Public Health Law Research, Temple University Beasley School of Law, Philadelphia, PA, USA
| | - Katie Moran-McCabe
- Center for Public Health Law Research, Temple University Beasley School of Law, Philadelphia, PA, USA
| | - Scott Burris
- Center for Public Health Law Research, Temple University Beasley School of Law, Philadelphia, PA, USA
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6
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Chan HF, Cheng Z, Mendolia S, Paloyo AR, Tani M, Proulx D, Savage DA, Torgler B. Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UK. Sci Rep 2024; 14:1790. [PMID: 38245576 PMCID: PMC10799952 DOI: 10.1038/s41598-024-51854-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
During the COVID-19 pandemic, several governments tried to contain the spread of SARS-CoV-2, the virus that causes COVID-19, with lockdowns that prohibited leaving one's residence unless carrying out a few essential services. We investigate the relationship between limitations to mobility and mental health in the UK during the first year and a half of the pandemic using a unique combination of high-frequency mobility data from Google and monthly longitudinal data collected through the Understanding Society survey. We find a strong and statistically robust correlation between mobility data and mental health survey data and show that increased residential stationarity is associated with the deterioration of mental wellbeing even when regional COVID-19 prevalence and lockdown stringency are controlled for. The relationship is heterogeneous, as higher levels of distress are seen in young, healthy people living alone; and in women, especially if they have young children.
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Affiliation(s)
- Ho Fai Chan
- School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, 4000, Australia.
- Centre for Behavioural Economics, Society and Technology (BEST), Brisbane, QLD, 4000, Australia.
- Centre for Behavioural Insights for Technology Adoption (BITA), Brisbane, QLD, 4000, Australia.
| | - Zhiming Cheng
- Social Policy Research Centre, University of New South Wales, Kensington, NSW, 2052, Australia
- Department of Management, Macquarie Business School, Macquarie University, Sydney, NSW, 2109, Australia
| | - Silvia Mendolia
- Department of Economics, Social Studies and Applied Mathematics and Statistics, University of Turin, Turin, Italy
| | | | | | - Damon Proulx
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
| | - David A Savage
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
| | - Benno Torgler
- School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- Centre for Behavioural Economics, Society and Technology (BEST), Brisbane, QLD, 4000, Australia
- Centre for Behavioural Insights for Technology Adoption (BITA), Brisbane, QLD, 4000, Australia
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
- CREMA - Center for Research in Economics, Management and the Arts, Basel, Switzerland
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7
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Hu K, Zhang L. Challenges and Opportunities Associated with Lifting the Zero COVID-19 Policy in China. EXPLORATORY RESEARCH AND HYPOTHESIS IN MEDICINE 2024; 9:71-75. [PMID: 38572142 PMCID: PMC10989839 DOI: 10.14218/erhm.2023.00002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2023]
Abstract
Chinese government lifted its "Zero COVID-19" policy in December 2022. The estimated COVDI-19 new cases and deaths after the policy change are 167-279 million (about 12.0% to 20.1% of the Chinese population) and 0.68-2.1 million, respectively. Recent data also revealed continuous drops in fertility rate and historically lowest growth in gross domestic production in China. Thus, balancing COVID-19 control and economic recovery in China is of paramount importance yet very difficult. Supply chain disruption, essential service reduction and shortage of intensive care units have been discussed as the challenges associated with lifting "Zero COVID-19" policy. The additional challenges may include triple epidemic of COVID-19, respiratory syncytial virus and influenza, mental health issues of healthcare providers, care givers and patients, impact on human mobility, lack of robust genomic and epidemiological data and long COVID-19. However, the policy-associated opportunities and other challenges are largely untouched, but warrant attention of and prompt reactions by the policy makers, healthcare providers, public health officials and other stakeholders. The associated benefits are quick reach of herd immunity, boost of economy and businesses activities and increase in social activities. At this moment, we must embrace the policy change, effectively mitigate its associated problems and timely and effectively maximize its associated benefits.
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Affiliation(s)
- Kun Hu
- Department of Pathology, Tufts Medical Center, Boston, MA, USA
| | - Lanjing Zhang
- Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
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8
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Crocker A, Strömbom D. Susceptible-Infected-Susceptible type COVID-19 spread with collective effects. Sci Rep 2023; 13:22600. [PMID: 38114694 PMCID: PMC10730724 DOI: 10.1038/s41598-023-49949-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Many models developed to forecast and attempt to understand the COVID-19 pandemic are highly complex, and few take collective behavior into account. As the pandemic progressed individual recurrent infection was observed and simpler susceptible-infected type models were introduced. However, these do not include mechanisms to model collective behavior. Here, we introduce an extension of the SIS model that accounts for collective behavior and show that it has four equilibria. Two of the equilibria are the standard SIS model equilibria, a third is always unstable, and a fourth where collective behavior and infection prevalence interact to produce either node-like or oscillatory dynamics. We then parameterized the model using estimates of the transmission and recovery rates for COVID-19 and present phase diagrams for fixed recovery rate and free transmission rate, and both rates fixed. We observe that regions of oscillatory dynamics exist in both cases and that the collective behavior parameter regulates their extent. Finally, we show that the system exhibits hysteresis when the collective behavior parameter varies over time. This model provides a minimal framework for explaining oscillatory phenomena such as recurring waves of infection and hysteresis effects observed in COVID-19, and other SIS-type epidemics, in terms of collective behavior.
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Affiliation(s)
- Amanda Crocker
- Department of Biology, Lafayette College, Easton, PA, 18042, USA
| | - Daniel Strömbom
- Department of Biology, Lafayette College, Easton, PA, 18042, USA.
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Agrawal V, Cantor J, Sood N, Whaley C. The impact of COVID-19 shelter-in-place policy responses on excess mortality. HEALTH ECONOMICS 2023; 32:2499-2515. [PMID: 37464737 DOI: 10.1002/hec.4737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 05/24/2023] [Accepted: 06/25/2023] [Indexed: 07/20/2023]
Abstract
As a way of slowing COVID-19 transmission, many countries and U.S. states implemented shelter-in-place (SIP) policies. However, the effects of SIP policies on public health are a priori ambiguous. Using an event study approach and data from 43 countries and all U.S. states, we measure changes in excess deaths following the implementation of COVID-19 shelter-in-place (SIP) policies. We do not find that countries or U.S. states that implemented SIP policies earlier had lower excess deaths. We do not observe differences in excess deaths before and after the implementation of SIP policies, even when accounting for pre-SIP COVID-19 death rates.
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Affiliation(s)
- Virat Agrawal
- University of Southern California, Los Angeles, California, USA
| | | | - Neeraj Sood
- University of Southern California, Los Angeles, California, USA
- National Bureau for Economic Research, Cambridge, Massachusetts, USA
| | - Christopher Whaley
- RAND Corporation, Santa Monica, California, USA
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island, USA
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10
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Wei Z, Zhuang J. On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2298-2311. [PMID: 36635059 DOI: 10.1111/risa.14093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID-19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game-theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two-group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long-term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask-wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.
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Affiliation(s)
- Zhiyuan Wei
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA
| | - Jun Zhuang
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA
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11
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Wallace J, Goldsmith-Pinkham P, Schwartz JL. Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic. JAMA Intern Med 2023; 183:916-923. [PMID: 37486680 PMCID: PMC10366951 DOI: 10.1001/jamainternmed.2023.1154] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/04/2023] [Indexed: 07/25/2023]
Abstract
Importance There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination; further data on these rates may be useful. Objective To assess political party affiliation and mortality rates for individuals during the initial 22 months of the COVID-19 pandemic. Design, Setting, and Participants A cross-sectional comparison of excess mortality between registered Republican and Democratic voters between March 2020 and December 2021 adjusted for age and state of voter registration was conducted. Voter and mortality data from Florida and Ohio in 2017 linked to mortality records for January 1, 2018, to December 31, 2021, were used in data analysis. Exposures Political party affiliation. Main Outcomes and Measures Excess weekly deaths during the COVID-19 pandemic adjusted for age, county, party affiliation, and seasonality. Results Between January 1, 2018, and December 31, 2021, there were 538 159 individuals in Ohio and Florida who died at age 25 years or older in the study sample. The median age at death was 78 years (IQR, 71-89 years). Overall, the excess death rate for Republican voters was 2.8 percentage points, or 15%, higher than the excess death rate for Democratic voters (95% prediction interval [PI], 1.6-3.7 percentage points). After May 1, 2021, when vaccines were available to all adults, the excess death rate gap between Republican and Democratic voters widened from -0.9 percentage point (95% PI, -2.5 to 0.3 percentage points) to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis; the excess death rate among Republican voters was 43% higher than the excess death rate among Democratic voters. The gap in excess death rates between Republican and Democratic voters was larger in counties with lower vaccination rates and was primarily noted in voters residing in Ohio. Conclusions and Relevance In this cross-sectional study, an association was observed between political party affiliation and excess deaths in Ohio and Florida after COVID-19 vaccines were available to all adults. These findings suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been factors in the severity and trajectory of the pandemic in the US.
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Affiliation(s)
- Jacob Wallace
- Yale School of Public Health, New Haven, Connecticut
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12
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Ge Y, Wu X, Zhang W, Wang X, Zhang D, Wang J, Liu H, Ren Z, Ruktanonchai NW, Ruktanonchai CW, Cleary E, Yao Y, Wesolowski A, Cummings DAT, Li Z, Tatem AJ, Lai S. Effects of public-health measures for zeroing out different SARS-CoV-2 variants. Nat Commun 2023; 14:5270. [PMID: 37644012 PMCID: PMC10465600 DOI: 10.1038/s41467-023-40940-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023] Open
Abstract
Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.
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Affiliation(s)
- Yong Ge
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
- Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
| | - Xilin Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Wenbin Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Xiaoli Wang
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Die Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang, China
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Haiyan Liu
- Marine Data Center, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Zhoupeng Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | | | | | - Eimear Cleary
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Yongcheng Yao
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- School of Mathematics and Statistics, Zhengzhou Normal University, Zhengzhou, China
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Derek A T Cummings
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Zhongjie Li
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
- Institute for Life Sciences, University of Southampton, Southampton, UK.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
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13
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Ling L, Ukkusuri SV. Investigating the effects of vaccine on COVID-19 disease propagation using a Bayesian approach. Sci Rep 2023; 13:13374. [PMID: 37591905 PMCID: PMC10435512 DOI: 10.1038/s41598-023-37972-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/30/2023] [Indexed: 08/19/2023] Open
Abstract
The causal impact of COVID-19 vaccine coverage on effective reproduction number R(t) under the disease control measures in the real-world scenario is understudied, making the optimal reopening strategy (e.g., when and which control measures are supposed to be conducted) during the recovery phase difficult to design. In this study, we examine the demographic heterogeneity and time variation of the vaccine effect on disease propagation based on the Bayesian structural time series analysis. Furthermore, we explore the role of non-pharmaceutical interventions (NPIs) and the entrance of the Delta variant of COVID-19 in the vaccine effect for U.S. counties. The analysis highlights several important findings: First, vaccine effects vary among the age-specific population and population densities. The vaccine effect for areas with high population density or core airport hubs is 2 times higher than for areas with low population density. Besides, areas with more older people need a high vaccine coverage to help them against the more contagious variants (e.g., the Delta variant). Second, the business restriction policy and mask requirement are more effective in preventing COVID-19 infections than other NPI measures (e.g., bar closure, gather ban, and restaurant restrictions) for areas with high population density and core airport hubs. Furthermore, the mask requirement consistently amplifies the vaccine effects against disease propagation after the presence of contagious variants. Third, areas with a high percentage of older people are suggested to postpone relaxing the restaurant restriction or gather ban since they amplify the vaccine effect against disease infections. Such empirical insights assist recovery phases of the pandemic in designing more efficient reopening strategies, vaccine prioritization, and allocation policies.
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Affiliation(s)
- Lu Ling
- Lyles School of Civil Engineering, Purdue University, West Lafayette city, 47906, USA
| | - Satish V Ukkusuri
- Lyles School of Civil Engineering, Purdue University, West Lafayette city, 47906, USA.
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14
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Yang L, Wang Z, Wang L, Vrancken B, Wang R, Wei Y, Rader B, Wu CH, Chen Y, Wu P, Li B, Lin Q, Dong L, Cui Y, Shi M, Brownstein JS, Stenseth NC, Yang R, Tian H. Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2. Proc Natl Acad Sci U S A 2023; 120:e2305403120. [PMID: 37549270 PMCID: PMC10434302 DOI: 10.1073/pnas.2305403120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/07/2023] [Indexed: 08/09/2023] Open
Abstract
Continually emerging SARS-CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID-19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID-19 vaccine rollout, SARS-CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country-to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.
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Affiliation(s)
- Lingyue Yang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, CambridgeCB2 3EH, United Kingdom
| | - Bram Vrancken
- Department of Microbiology and Immunology, Rega Institute, Laboratory of Evolutionary and Computational Virology, KU Leuven, Leuven3000, Belgium
- Spatial Epidemiology Lab, Université Libre de Bruxelles, 1050Bruxelles, Belgium
| | - Ruixue Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Yuanlong Wei
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA02215
- Department of Epidemiology, Boston University School of Public Health, Boston, MA02118
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, SouthamptonSO17 1BJ, United Kingdom
| | - Yuyang Chen
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Peiyi Wu
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Qiushi Lin
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Lu Dong
- College of Life Sciences, Beijing Normal University, Beijing100875, China
| | - Yujun Cui
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing100071, China
| | - Mang Shi
- The Centre for Infection and Immunity Studies, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen518107, China
| | - John S. Brownstein
- Spatial Epidemiology Lab, Université Libre de Bruxelles, 1050Bruxelles, Belgium
- Harvard Medical School, Harvard University, Boston, MA02115
| | - Nils Chr. Stenseth
- The Centre for Pandemics and One-Health Research, Sustainable Health Unit, Faculty of Medicine, University of Oslo, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo0316, Norway
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
| | - Ruifu Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing100071, China
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
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15
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Zhou X, Zhang X, Santi P, Ratti C. Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S. Front Public Health 2023; 11:1198973. [PMID: 37601210 PMCID: PMC10434774 DOI: 10.3389/fpubh.2023.1198973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.
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Affiliation(s)
- Xiao Zhou
- Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
- Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Xiaohu Zhang
- Department of Urban Planning, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Paolo Santi
- Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Istituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Carlo Ratti
- Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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16
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Vilar JMG, Saiz L. Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series. SCIENCE ADVANCES 2023; 9:eadf0673. [PMID: 37450598 PMCID: PMC10348669 DOI: 10.1126/sciadv.adf0673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/14/2023] [Indexed: 07/18/2023]
Abstract
The ability to infer the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is crucial for multiple applications. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions (NPIs) with 0.93-day accuracy over the time span of a year.
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Affiliation(s)
- Jose M. G. Vilar
- Biofisika Institute (CSIC, UPV/EHU), University of the Basque Country (UPV/EHU), P.O. Box 644, 48080 Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Leonor Saiz
- Department of Biomedical Engineering, University of California, 451 E. Health Sciences Drive, Davis, CA 95616, USA
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17
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Leach D, Morris KJ, Fiecas MB, Tarr GAM. Sociodemographic effects on pandemic fatigue are multifaceted and context-specific: A longitudinal analysis of physical distancing adherence. J Public Health Res 2023; 12:22799036231189308. [PMID: 37529066 PMCID: PMC10387788 DOI: 10.1177/22799036231189308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Background Pandemic fatigue emerged early during the COVID-19 pandemic and remains a concern as new variants emerge and ongoing public health measures are needed to control them. A wide range of factors can affect pandemic fatigue, but empiric research indicating which may be most important to adherence in specific populations is lacking. Design & Methods We conducted a longitudinal study of changes in physical distancing in two cohorts: adults living with children <18 years and adults ≥50 years old. Six types of non-work, non-household contacts were ascertained at six times from April to October 2020. We used generalized estimating equations Poisson regression to estimate the one-week change in contact rate and how this differed based on sociodemographic characteristics. Results The rate of all contact types increased during the middle of the study period and decreased toward the end. Changes in contact rates over time differed according to several sociodemographic characteristics, including age, gender, race/ethnicity, education, household composition, and access to transportation. Furthermore, the factors influencing the rate of change in contact rates differed by the type or setting of the contact, for example contacts as a result of visiting another person's home versus during a retail outing. Conclusions These results provide evidence for potential mechanisms by which pandemic fatigue has resulted in lower physical distancing adherence.
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Affiliation(s)
- Damon Leach
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Keeley J Morris
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Mark B Fiecas
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Gillian AM Tarr
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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18
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Liu XF, Wang ZZ, Xu XK, Wu Y, Zhao Z, Deng H, Wang P, Chao N, Huang YHC. The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors. EPJ DATA SCIENCE 2023; 12:17. [PMID: 37284234 PMCID: PMC10240109 DOI: 10.1140/epjds/s13688-023-00391-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 05/12/2023] [Indexed: 06/08/2023]
Abstract
Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming "digital residents." This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors. Supplementary Information The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.
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Affiliation(s)
- Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, 18 Tat Hong Avenue, Kowloon, Hong Kong SAR China
| | - Zhen-Zhen Wang
- School of Communication, Shenzhen University, 3688 Nanhai Avenue, 518060 Shenzhen, China
| | - Xiao-Ke Xu
- Center for Computational Communication Research, Beijing Normal University, Zhuhai, China
| | - Ye Wu
- Center for Computational Communication Research, Beijing Normal University, Zhuhai, China
| | - Zhidan Zhao
- School of Engineering, Shantou University, Shantou, China
| | - Huarong Deng
- OPPO Internet Advertising Technology, Shenzhen, China
| | - Ping Wang
- OPPO Internet Advertising Technology, Shenzhen, China
| | - Naipeng Chao
- School of Communication, Shenzhen University, 3688 Nanhai Avenue, 518060 Shenzhen, China
| | - Yi-Hui C. Huang
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, 18 Tat Hong Avenue, Kowloon, Hong Kong SAR China
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19
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White RC, Luo R, Rothenberg R. Nonpharmaceutical Interventions in Georgia: Public Health Implications. South Med J 2023; 116:383-389. [PMID: 37137470 PMCID: PMC10143397 DOI: 10.14423/smj.0000000000001552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
OBJECTIVES As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths. METHODS Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate. RESULTS We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome. CONCLUSIONS Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.
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Affiliation(s)
- Renee C White
- From the Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta
| | - Ruiyan Luo
- From the Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta
| | - Richard Rothenberg
- From the Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta
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20
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Stype AC, Yaya ME, Osika J. Non-pharmaceutical Interventions and COVID-19: Do County- and State-Level Policies Predict the Spread of COVID-19? JOURNAL OF ECONOMICS, RACE, AND POLICY 2023; 6:126-142. [PMID: 36816713 PMCID: PMC9930035 DOI: 10.1007/s41996-022-00112-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/03/2022] [Accepted: 12/23/2022] [Indexed: 02/17/2023]
Abstract
This study examines the impact of county- and state-level policies on the spread and severity of COVID-19 in communities in the USA during the first wave of the COVID-19 pandemic. We use county-level COVID-19 death and case data to examine the impact of county- and state-level mandates and non-pharmaceutical interventions (NPIs) on the spread and severity of COVID-19. Following previous work by Amuendo-Dorantes et al. (2020), we utilize a strategy that incorporates the duration of NPI implementation within a county. Specifically, we examine aggregated measures of mask mandates, daycare closures, stay-at-home orders, and restaurant and bar closures. In addition to the implementation and duration of NPI policy, we examine the role of pre-existing factors that contribute to social determinants of health in a locality. We incorporate information on the incidence of prior health conditions, socio-economic factors, and demographics including racial and ethnic composition, share of immigrant population of counties, and state governance in our estimations. To alleviate the possible endogeneity of COVID-19 outcomes and NPIs, we use instrumental variable estimation and our results show that collectively NPIs decreased the intensity of the pandemic by decreasing the total deaths and cases. Furthermore, we find the magnitude of the impact of NPIs increases the longer they are implemented. We also estimate a specification that allows for heterogeneity of NPI impact based on the racial and ethnic composition of counties. Our results suggest that NPIs have a non-uniform impact in counties with different racial and ethnic compositions.
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Affiliation(s)
- Amanda C. Stype
- grid.255399.10000000106743006Eastern Michigan University, 703 Pray-Harrold, Ypsilanti, MI 48197 USA
| | - Mehmet E. Yaya
- grid.255399.10000000106743006Eastern Michigan University, 703 Pray-Harrold, Ypsilanti, MI 48197 USA
| | - Jayson Osika
- grid.255399.10000000106743006Eastern Michigan University, 703 Pray-Harrold, Ypsilanti, MI 48197 USA
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21
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Liu L, Zhang Z, Wang H, Wang S, Zhuang S, Duan J. Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes. PLoS One 2023; 18:e0276906. [PMID: 36791127 PMCID: PMC9931149 DOI: 10.1371/journal.pone.0276906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/15/2022] [Indexed: 02/16/2023] Open
Abstract
The efficacy of government interventions in epidemic has become a hot subject since the onset of COVID-19. There is however much variation in the results quantifying the effects of interventions, which is partly related to the varying modelling approaches employed by existing studies. Among the many factors affecting the modelling results, people's voluntary behavior change is less examined yet likely to be widespread. This paper therefore aims to analyze how the choice of modelling approach, in particular how voluntary behavior change is accounted for, would affect the intervention effect estimation. We conduct the analysis by experimenting different modelling methods on a same data set composed of the 500 most infected U.S. counties. We compare the most frequently used methods from the two classes of modelling approaches, which are Bayesian hierarchical model from the class of computational approach and difference-in-difference from the class of natural experimental approach. We find that computational methods that do not account for voluntary behavior changes are likely to produce larger estimates of intervention effects as assumed. In contrast, natural experimental methods are more likely to extract the true effect of interventions by ruling out simultaneous behavior change. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Our work can inform the methodological choice of future research on this topic, as well as more robust re-interpretation of existing works, to facilitate both future epidemic response plans and the science of public health.
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Affiliation(s)
- Lun Liu
- School of Government, Peking University, Beijing, China
- Institute of Public Governance, Peking University, Beijing, China
| | - Zhu Zhang
- School of Government, Peking University, Beijing, China
| | - Hui Wang
- School of Architecture, Tsinghua University, Beijing, China
- * E-mail:
| | - Shenhao Wang
- Department of Urban and Regional Planning, University of Florida, Gainesville, Florida, United States of America
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | | | - Jishan Duan
- Graduate School of Architecture, Planning and Preservation, Columbia University, New York, New York, United States of America
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22
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Li L, Taeihagh A, Tan SY. A scoping review of the impacts of COVID-19 physical distancing measures on vulnerable population groups. Nat Commun 2023; 14:599. [PMID: 36737447 PMCID: PMC9897623 DOI: 10.1038/s41467-023-36267-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Most governments have enacted physical or social distancing measures to control COVID-19 transmission. Yet little is known about the socio-economic trade-offs of these measures, especially for vulnerable populations, who are exposed to increased risks and are susceptible to adverse health outcomes. To examine the impacts of physical distancing measures on the most vulnerable in society, this scoping review screened 39,816 records and synthesised results from 265 studies worldwide documenting the negative impacts of physical distancing on older people, children/students, low-income populations, migrant workers, people in prison, people with disabilities, sex workers, victims of domestic violence, refugees, ethnic minorities, and people from sexual and gender minorities. We show that prolonged loneliness, mental distress, unemployment, income loss, food insecurity, widened inequality and disruption of access to social support and health services were unintended consequences of physical distancing that impacted these vulnerable groups and highlight that physical distancing measures exacerbated the vulnerabilities of different vulnerable populations.
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Affiliation(s)
- Lili Li
- Policy Systems Group, Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore
| | - Araz Taeihagh
- Policy Systems Group, Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore.
| | - Si Ying Tan
- Alexandra Research Centre for Healthcare in The Virtual Environment (ARCHIVE), Department of Healthcare Redesign, Alexandra Hospital, National University Health System, Singapore, Singapore
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23
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Perez C, Karmakar S. An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic. SOCIAL NETWORK ANALYSIS AND MINING 2023; 13:51. [PMID: 36937491 PMCID: PMC10016178 DOI: 10.1007/s13278-023-01053-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 02/19/2023] [Accepted: 02/24/2023] [Indexed: 03/17/2023]
Abstract
COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data were also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.
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Affiliation(s)
- Christopher Perez
- Department of Statistics, University of Florida, Gainesville, FL 32601 USA
| | - Sayar Karmakar
- Department of Statistics, University of Florida, Gainesville, FL 32601 USA
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24
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Community Mitigation Strategies, Mobility, and COVID-19 Incidence Across Three Waves in the United States in 2020. Epidemiology 2023; 34:131-139. [PMID: 36137192 PMCID: PMC9811991 DOI: 10.1097/ede.0000000000001553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Summarizing the impact of community-based mitigation strategies and mobility on COVID-19 infections throughout the pandemic is critical for informing responses and future infectious disease outbreaks. Here, we employed time-series analyses to empirically investigate the relationships between mitigation strategies and mobility on COVID-19 incident cases across US states during the first three waves of infections. METHODS We linked data on daily COVID-19 incidence by US state from March to December 2020 with the stringency index, a well-known index capturing the strictness of mitigation strategies, and the trip ratio, which measures the ratio of the number of trips taken per day compared with the same day in 2019. We utilized multilevel models to determine the relative impacts of policy stringency and the trip ratio on COVID-19 cumulative incidence and the effective reproduction number. We stratified analyses by three waves of infections. RESULTS Every five-point increase in the stringency index was associated with 2.89% (95% confidence interval = 1.52, 4.26%) and 5.01% (3.02, 6.95%) reductions in COVID-19 incidence for the first and third waves, respectively. Reducing the number of trips taken by 50% compared with the same time in 2019 was associated with a 16.2% (-0.07, 35.2%) decline in COVID-19 incidence at the state level during the second wave and 19.3% (2.30, 39.0%) during the third wave. CONCLUSIONS Mitigation strategies and reductions in mobility are associated with marked health gains through the reduction of COVID-19 infections, but we estimate variable impacts depending on policy stringency and levels of adherence.
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Zhunis A, Mai TD, Kim S. Responses to COVID-19 with probabilistic programming. Front Public Health 2022; 10:953472. [PMID: 36478717 PMCID: PMC9720399 DOI: 10.3389/fpubh.2022.953472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/01/2022] [Indexed: 11/22/2022] Open
Abstract
The COVID-19 pandemic left its unique mark on the twenty-first century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major initial non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.
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Affiliation(s)
- Assem Zhunis
- School of Computing, KAIST, Daejeon, South Korea,Data Science Group, Institute for Basic Science, Daejeon, South Korea
| | - Tung-Duong Mai
- School of Computing, KAIST, Daejeon, South Korea,Data Science Group, Institute for Basic Science, Daejeon, South Korea,Samsung Electronics, Seoul, South Korea
| | - Sundong Kim
- Data Science Group, Institute for Basic Science, Daejeon, South Korea,AI Graduate School, GIST, Gwangju, South Korea,*Correspondence: Sundong Kim
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Liu L, Wang H, Zhang Z, Zhang W, Zhuang S, Wang S, Silva EA, Lv T, Chio CO, Wang Y, Dao R, Tang C, Ao-Ieong OI. Infectiousness of places - Impact of multiscale human activity places in the transmission of COVID-19. NPJ URBAN SUSTAINABILITY 2022; 2:28. [PMID: 37521773 PMCID: PMC9630073 DOI: 10.1038/s42949-022-00074-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 10/20/2022] [Indexed: 08/01/2023]
Abstract
COVID-19 raises attention to epidemic transmission in various places. This study analyzes the transmission risks associated with human activity places at multiple scales, including different types of settlements and eleven types of specific establishments (restaurants, bars, etc.), using COVID-19 data in 906 urban areas across four continents. Through a difference-in-difference approach, we identify the causal effects of activities at various places on epidemic transmission. We find that at the micro-scale, though the transmission risks at different establishments differ across countries, sports, entertainment, and catering establishments are generally more infectious. At the macro-scale, contradicting common beliefs, it is consistent across countries that transmission does not increase with settlement size and density. It is also consistent that specific establishments play a lesser role in transmission in larger settlements, suggesting more transmission happening elsewhere. These findings contribute to building a system of knowledge on the linkage between places, human activities, and disease transmission.
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Affiliation(s)
- Lun Liu
- School of Government, Peking University, Beijing, China
- Institute of Public Governance, Peking University, Beijing, China
| | - Hui Wang
- School of Architecture, Tsinghua University, Beijing, China
| | - Zhu Zhang
- School of Government, Peking University, Beijing, China
| | - Weiyi Zhang
- School of Government, Peking University, Beijing, China
| | | | - Shenhao Wang
- Department of Urban and Regional Planning, University of Florida, Gainesville, FL USA
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Elisabete A. Silva
- Department of Land Economy, University of Cambridge, Cambridge, UK
- Lab of Interdisciplinary Spatial Analysis, University of Cambridge, Cambridge, UK
| | - Tingmiao Lv
- School of Government, Peking University, Beijing, China
| | - Chi On Chio
- School of Government, Peking University, Beijing, China
| | - Yifan Wang
- School of Government, Peking University, Beijing, China
| | - Rina Dao
- School of Government, Peking University, Beijing, China
| | - Chuchang Tang
- School of Government, Peking University, Beijing, China
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Chen S, Murphy EA, Pendergrass AG, Sukhu AC, Eng D, Jurkiewicz M, Mohammed I, Rand S, White LJ, Hupert N, Yang YJ. Estimating the Effectiveness of Shielding during Pregnancy against SARS-CoV-2 in New York City during the First Year of the COVID-19 Pandemic. Viruses 2022; 14:v14112408. [PMID: 36366506 PMCID: PMC9697040 DOI: 10.3390/v14112408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 01/31/2023] Open
Abstract
Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.
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Affiliation(s)
- Siyu Chen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX1 3QD, UK
- Correspondence: (S.C.); (Y.J.Y.)
| | - Elisabeth A. Murphy
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Ashley C. Sukhu
- New York Presbyterian-Weill Cornell Medicine, New York, NY 10065, USA
| | - Dorothy Eng
- New York Presbyterian-Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Iman Mohammed
- Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Sophie Rand
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Lisa J. White
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX1 3QD, UK
| | - Nathaniel Hupert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX1 3QD, UK
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Yawei J. Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
- New York Presbyterian-Weill Cornell Medicine, New York, NY 10065, USA
- Correspondence: (S.C.); (Y.J.Y.)
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Ferrari D, Stillman S, Tonin M. Assessing the impact of COVID-19 mass testing in South Tyrol using a semi-parametric growth model. Sci Rep 2022; 12:17952. [PMID: 36289286 PMCID: PMC9605953 DOI: 10.1038/s41598-022-21292-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/26/2022] [Indexed: 01/24/2023] Open
Abstract
Mass antigen testing has been proposed as a possible cost-effective tool to contain the COVID-19 pandemic. We test the impact of a voluntary mass testing campaign implemented in the Italian region of South Tyrol on the spread of the virus in the following months. We do so by using an innovative empirical approach which embeds a semi-parametric growth model-where COVID-19 transmission dynamics are allowed to vary across regions and to be impacted by the implementation of the mass testing campaign-into a synthetic control framework which creates an appropriate control group of other Italian regions. Our results suggest that mass testing campaigns are useful instruments for mitigating the pandemic.
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Affiliation(s)
- Davide Ferrari
- grid.34988.3e0000 0001 1482 2038Faculty of Economics and Management, Free University of Bozen-Bolzano, Piazza Università 1, 39100 Bolzano, Italy
| | - Steven Stillman
- grid.34988.3e0000 0001 1482 2038Faculty of Economics and Management, Free University of Bozen-Bolzano, Piazza Università 1, 39100 Bolzano, Italy
| | - Mirco Tonin
- grid.34988.3e0000 0001 1482 2038Faculty of Economics and Management, Free University of Bozen-Bolzano, Piazza Università 1, 39100 Bolzano, Italy ,grid.11469.3b0000 0000 9780 0901Research Institute for the Evaluation of Public Policies, Bruno Kessler Foundation, Trento, Italy
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A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic. PLoS One 2022; 17:e0265289. [PMID: 36170272 PMCID: PMC9518862 DOI: 10.1371/journal.pone.0265289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures and the long-term causal contribution of each NPI are still a topic of debate. We present a method to rigorously study the effectiveness of interventions on the rate of the time-varying reproduction number Rt and on human mobility, considered here as a proxy measure of policy adherence and social distancing. We frame our model using a causal inference approach to quantify the impact of five governmental interventions introduced until June 2020 to control the outbreak in 113 countries: confinement, school closure, mask wearing, cultural closure, and work restrictions. Our results indicate that mobility changes are more accurately predicted when compared to reproduction number. All NPIs, except for mask wearing, significantly affected human mobility trends. From these, schools and cultural closure mandates showed the largest effect on social distancing. We also found that closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction on Rt after their initiation, which was not observed with the other social distancing measures. Our results are robust and consistent across different model specifications and can shed more light on the impact of individual NPIs.
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Briones J, Wang Y, Prawjaeng J, Wee HL, Kairu A, Orangi S, Barasa E, Teerawattananon Y. A Data-Driven Analysis of the Economic Cost of Non-Pharmaceutical Interventions: A Cross-Country Comparison of Kenya, Singapore, and Thailand. Int J Public Health 2022; 67:1604854. [PMID: 35837381 PMCID: PMC9273740 DOI: 10.3389/ijph.2022.1604854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: To estimate the economic impact of border closure and social distancing by estimating the decline of gross domestic product (GDP) in Kenya, Singapore and Thailand.Methods: We analysed secondary data retrospectively. To calculate impact of NPIs on GDP, the relationship between GDP and stock market index was examined using ordinary least squares (OLS). Then, autoregressive and moving averages (ARMA) model was used to examine the impact of NPI on stock market index. The change in GDP due to NPIs was derived by multiplying coefficients of OLS and ARMA models.Results: An increase in stock market index correlated with an increase in GDP, while both social distancing and border closure negatively correlated with stock market index. Implementation of NPIs correlated with the decline in GDP. Thai border closure had a greater decline in GDP than social distancing; Kenya exhibited the same trends; Singapore had the opposite trend.Conclusion: We quantified the magnitude of economic impact of NPIs in terms of GDP decline by linking stock market index and GDP. This approach may be applicable in other settings.
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Affiliation(s)
- Jamaica Briones
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Yi Wang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- *Correspondence: Yi Wang,
| | - Juthamas Prawjaeng
- Health Intervention and Technology Assessment Program, Ministry of Public Health, Nonthaburi, Thailand
| | - Hwee Lin Wee
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Angela Kairu
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
| | - Stacey Orangi
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
| | - Edwine Barasa
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Yot Teerawattananon
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Health Intervention and Technology Assessment Program, Ministry of Public Health, Nonthaburi, Thailand
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31
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Her PH, Saeed S, Tram KH, Bhatnagar SR. Novel mobility index tracks COVID-19 transmission following stay-at-home orders. Sci Rep 2022; 12:7654. [PMID: 35538129 PMCID: PMC9088135 DOI: 10.1038/s41598-022-10941-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/12/2022] [Indexed: 12/13/2022] Open
Abstract
Considering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. Using a functional principal component analysis, we created a multidimensional mobility index (MI) using six metrics compiled by SafeGraph from all counties in Illinois, Ohio, Michigan and Indiana between January 1 to December 8, 2020. Changes in mobility were defined as a time-updated 7-day rolling average. Associations between our MI and COVID-19 cases were estimated using a quasi-Poisson hierarchical generalized additive model adjusted for population density and the COVID-19 Community Vulnerability Index. Individual mobility metrics varied significantly by counties and by calendar time. More than 50% of the variability in the data was explained by the first principal component by each state, indicating good dimension reduction. While an individual metric of mobility was not associated with surges of COVID-19, our MI was independently associated with COVID-19 cases in all four states given varying time-lags. Following the expiration of stay-at-home orders, a single metric of mobility was not sensitive enough to capture the complexity of human interactions. Monitoring mobility can be an important public health tool, however, it should be modelled as a multidimensional construct.
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Affiliation(s)
- Peter Hyunwuk Her
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sahar Saeed
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, USA.,Department of Public Health Sciences, Queen's University, Ontario, Canada
| | - Khai Hoan Tram
- Division of Infectious Diseases, Department of Medicine, University of Washington, Seattle, USA
| | - Sahir R Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. .,Department of Diagnostic Radiology, McGill University, Montreal, Canada.
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Chahouri A, Elouahmani N, Ouchene H. Recent progress in marine noise pollution: A thorough review. CHEMOSPHERE 2022; 291:132983. [PMID: 34801565 DOI: 10.1016/j.chemosphere.2021.132983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/09/2021] [Accepted: 11/17/2021] [Indexed: 06/13/2023]
Abstract
The increase in urbanization and the progressive development of marine industries have led to the appearance of a new kind of pollution called "noise pollution". This pollution exerts an increasing pressure on marine mammals, fish species, and invertebrates, which constitutes a new debate that must be controlled in a sustainable way by environmental and noise approaches with the objective of preserving marine and human life. Despite, noise pollution can travel long distances underwater, cover large areas, and have secondary effects on marine animals; by masking their ability to hear their prey or predators, finding their way, or connecting group members. During the COVID-19 pandemic, except for the transportation of essential goods and emergency services, all the public transport services were suspended including aircraft and ships. This lockdown has impacted positively on the marine environment through reduction of the noise sources. In this article, we are interested in noise pollution in general, its sources, impacts, and the management and future actions to follow. And since this pollution is not studied in Morocco, we focused on the different sources that can generate it on the Moroccan coasts. This is the first review article, which focuses on the impact of the COVID 19 pandemic on this type of pollution in the marine environment; which we aim to identify the impact of this pandemic on underwater noise and marine species. Finally, and given the increase in noise levels, preventive management, both at the national and international level, is required before irreversible damage is caused to biodiversity and the marine ecosystem.
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Affiliation(s)
- Abir Chahouri
- Aquatic System Laboratory: Marine and Continental Environment, Faculty of Sciences Agadir, Department of Biology, Ibn Zohr University, Agadir, Morocco.
| | - Nadia Elouahmani
- Aquatic System Laboratory: Marine and Continental Environment, Faculty of Sciences Agadir, Department of Biology, Ibn Zohr University, Agadir, Morocco
| | - Hanan Ouchene
- Aquatic System Laboratory: Marine and Continental Environment, Faculty of Sciences Agadir, Department of Biology, Ibn Zohr University, Agadir, Morocco
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Multiple COVID-19 Waves and Vaccination Effectiveness in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042282. [PMID: 35206474 PMCID: PMC8871705 DOI: 10.3390/ijerph19042282] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 01/27/2023]
Abstract
(1) Background: The coronavirus 2019 (COVID-19) pandemic has caused multiple waves of cases and deaths in the United States (US). The wild strain, the Alpha variant (B.1.1.7) and the Delta variant (B.1.617.2) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were the principal culprits behind these waves. To mitigate the pandemic, the vaccination campaign was started in January 2021. While the vaccine efficacy is less than 1, breakthrough infections were reported. This work aims to examine the effects of the vaccination across 50 US states and the District of Columbia. (2) Methods: Based on the classic Susceptible—Exposed—Infectious–Recovered (SEIR) model, we add a delay class between infectious and death, a death class and a vaccinated class. We compare two special cases of our new model to simulate the effects of the vaccination. The first case expounds the vaccinated individuals with full protection or not, compared to the second case where all vaccinated individuals have the same level of protection. (3) Results: Through fitting the two approaches to reported COVID-19 deaths in all 50 US states and the District of Columbia, we found that these two approaches are equivalent. We calculate that the death toll could be 1.67–3.33 fold in most states if the vaccine was not available. The median and mean infection fatality ratio are estimated to be approximately 0.6 and 0.7%. (4) Conclusions: The two approaches we compared were equivalent in evaluating the effectiveness of the vaccination campaign in the US. In addition, the effect of the vaccination campaign was significant, with a large number of deaths averted.
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Rabiu M, Iyaniwura SA. Assessing the potential impact of immunity waning on the dynamics of COVID-19 in South Africa: an endemic model of COVID-19. NONLINEAR DYNAMICS 2022; 109:203-223. [PMID: 35095199 PMCID: PMC8788409 DOI: 10.1007/s11071-022-07225-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/09/2022] [Indexed: 05/05/2023]
Abstract
We developed an endemic model of COVID-19 to assess the impact of vaccination and immunity waning on the dynamics of the disease. Our model exhibits the phenomenon of backward bifurcation and bi-stability, where a stable disease-free equilibrium coexists with a stable endemic equilibrium. The epidemiological implication of this is that the control reproduction number being less than unity is no longer sufficient to guarantee disease eradication. We showed that this phenomenon could be eliminated by either increasing the vaccine efficacy or by reducing the disease transmission rate (adhering to non-pharmaceutical interventions). Furthermore, we numerically investigated the impacts of vaccination and waning of both vaccine-induced immunity and post-recovery immunity on the disease dynamics. Our simulation results show that the waning of vaccine-induced immunity has more effect on the disease dynamics relative to post-recovery immunity waning and suggests that more emphasis should be on reducing the waning of vaccine-induced immunity to eradicate COVID-19.
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Affiliation(s)
- Musa Rabiu
- School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban, South Africa
| | - Sarafa A Iyaniwura
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, BC Canada
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Lalwani P, Araujo-Castillo RV, Ganoza CA, Salgado BB, Pereira Filho IV, da Silva DSS, de Morais TBDN, Jordão MF, Ortiz JV, Barbosa ARC, Sobrinho WBS, Cordeiro IB, de Souza Neto JN, de Assunção EN, da Costa CF, de Souza PE, de Albuquerque BC, Astofi-Filho S, Lalwani JDB. High anti-SARS-CoV-2 antibody seroconversion rates before the second wave in Manaus, Brazil, and the protective effect of social behaviour measures: results from the prospective DETECTCoV-19 cohort. LANCET GLOBAL HEALTH 2021; 9:e1508-e1516. [PMID: 34678195 PMCID: PMC8525986 DOI: 10.1016/s2214-109x(21)00355-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 07/11/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
Background The city of Manaus, Brazil, has seen two collapses of the health system due to the COVID-19 pandemic. We report anti-SARS-CoV-2 nucleocapsid IgG antibody seroconversion rates and associated risk factors in Manaus residents before the second wave of the epidemic in Brazil. Methods A convenience sample of adult (aged ≥18 years) residents of Manaus was recruited through online and university website advertising into the DETECTCoV-19 study cohort. The current analysis of seroconversion included a subgroup of DETECTCoV-19 participants who had at least two serum sample collections separated by at least 4 weeks between Aug 19 and Oct 2, 2020 (visit 1), and Oct 19 and Nov 27, 2020 (visit 2). Those who reported (or had no data on) having a COVID-19 diagnosis before visit 1, and who were positive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at visit 1 were excluded. Using an in-house ELISA, the reactivity index (RI; calculated as the optical density ratio of the sample to the negative control) for serum anti-SARS-CoV-2 nucleocapsid IgG antibodies was measured at both visits. We calculated the incidence of seroconversion (defined as RI values ≤1·5 at visit 1 and ≥1·5 at visit 2, and a ratio >2 between the visit 2 and visit 1 RI values) during the study period, as well as incidence rate ratios (IRRs) through cluster-corrected and adjusted Poisson regression models to analyse associations between seroconversion and variables related to sociodemographic characteristics, health access, comorbidities, COVID-19 exposure, protective behaviours, and symptoms. Findings 2496 DETECTCoV-19 cohort participants returned for a follow-up visit between Oct 19 and Nov 27, 2020, of whom 204 reported having COVID-19 before the first visit and 24 had no data regarding previous disease status. 559 participants were seropositive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at baseline. Of the remaining 1709 participants who were seronegative at baseline, 71 did not meet the criteria for seroconversion and were excluded from the analyses. Among the remaining 1638 participants who were seronegative at baseline, 214 showed seroconversion at visit 2. The seroconversion incidence was 13·06% (95% CI 11·52–14·79) overall and 6·78% (5·61–8·10) for symptomatic seroconversion, over a median follow-up period of 57 days (IQR 54–61). 48·1% of seroconversion events were estimated to be asymptomatic. The sample had higher proportions of affluent and higher-educated people than those reported for the Manaus city population. In the fully adjusted and corrected model, risk factors for seroconversion before visit 2 were having a COVID-19 case in the household (IRR 1·49 [95% CI 1·21–1·83]), not wearing a mask during contact with a person with COVID-19 (1·25 [1·09–1·45]), relaxation of physical distancing (1·31 [1·05–1·64]), and having flu-like symptoms (1·79 [1·23–2·59]) or a COVID-19 diagnosis (3·57 [2·27–5·63]) between the first and second visits, whereas working remotely was associated with lower incidence (0·74 [0·56–0·97]). Interpretation An intense infection transmission period preceded the second wave of COVID-19 in Manaus. Several modifiable behaviours increased the risk of seroconversion, including non-compliance with non-pharmaceutical interventions measures such as not wearing a mask during contact, relaxation of protective measures, and non-remote working. Increased testing in high-transmission areas is needed to provide timely information about ongoing transmission and aid appropriate implementation of transmission mitigation measures. Funding Ministry of Education, Brazil; Fundação de Amparo à Pesquisa do Estado do Amazonas; Pan American Health Organization (PAHO)/WHO.
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Affiliation(s)
- Pritesh Lalwani
- Instituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil.
| | | | - Christian A Ganoza
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Ivanildo Vieira Pereira Filho
- Instituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil; Faculdade de Ciências Farmacêuticas, Universidade Federal do Amazonas, Manaus, Brazil
| | | | | | | | | | - Aguyda Rayany Cavalcante Barbosa
- Instituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil; Instituto de Ciências Biológicas, Universidade Federal do Amazonas, Manaus, Brazil
| | | | | | | | | | | | - Pedro Elias de Souza
- Programa de Pós-Graduação em Cirurgia, Faculdade de Medicina, Universidade Federal do Amazonas, Manaus, Brazil
| | | | - Spartaco Astofi-Filho
- Instituto de Ciências Biológicas, Universidade Federal do Amazonas, Manaus, Brazil; Centro de Apoio Multidisciplinar, Universidade Federal do Amazonas, Manaus, Brazil
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Ye M, Zino L, Rizzo A, Cao M. Game-theoretic modeling of collective decision making during epidemics. Phys Rev E 2021; 104:024314. [PMID: 34525543 DOI: 10.1103/physreve.104.024314] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/30/2021] [Indexed: 11/07/2022]
Abstract
The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral-epidemic model, in which an interplay of realistic factors shapes the coevolution of individual decision making and epidemics on a network. Although such a coevolution is deeply intertwined in the real world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our paradigm offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows us to provide analytical insights on the epidemic process and to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.
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Affiliation(s)
- Mengbin Ye
- School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia
| | - Lorenzo Zino
- Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, Netherlands
| | - Alessandro Rizzo
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy
| | - Ming Cao
- Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, Netherlands
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Spatially Refined Time-Varying Reproduction Numbers of COVID-19 by Health District in Georgia, USA, March-December 2020. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2021; 2:179-197. [PMID: 36417182 PMCID: PMC9620885 DOI: 10.3390/epidemiologia2020014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 05/17/2021] [Accepted: 05/25/2021] [Indexed: 01/08/2023]
Abstract
This study quantifies the transmission potential of SARS-CoV-2 across public health districts in Georgia, USA, and tests if per capita cumulative case count varies across counties. To estimate the time-varying reproduction number, Rt of SARS-CoV-2 in Georgia and its 18 public health districts, we apply the R package 'EpiEstim' to the time series of historical daily incidence of confirmed cases, 2 March-15 December 2020. The epidemic curve is shifted backward by nine days to account for the incubation period and delay to testing. Linear regression is performed between log10-transformed per capita cumulative case count and log10-transformed population size. We observe Rt fluctuations as state and countywide policies are implemented. Policy changes are associated with increases or decreases at different time points. Rt increases, following the reopening of schools for in-person instruction in August. Evidence suggests that counties with lower population size had a higher per capita cumulative case count on June 15 (slope = -0.10, p = 0.04) and October 15 (slope = -0.05, p = 0.03), but not on August 15 (slope = -0.04, p = 0.09), nor December 15 (slope = -0.02, p = 0.41). We found extensive community transmission of SARS-CoV-2 across all 18 health districts in Georgia with median 7-day-sliding window Rt estimates between 1 and 1.4 after March 2020.
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Parent-Child Relationships and the COVID-19 Pandemic: An Exploratory Qualitative Study with Parents in Early, Middle, and Late Adulthood. JOURNAL OF ADULT DEVELOPMENT 2021; 28:251-263. [PMID: 34035642 PMCID: PMC8136369 DOI: 10.1007/s10804-021-09381-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2021] [Indexed: 11/05/2022]
Abstract
The impact of the COVID-19 pandemic on families is currently unknown. Parents and children have experienced a variety of changes as public health interventions have been implemented to slow the spread of the virus. The current exploratory qualitative study recruited parents (n = 365) in early (ages 20–34), middle (ages 35–64), and late (ages 65 and older) adulthood to understand how the early weeks of the pandemic influenced their parent–child relationships. Participants completed an online survey between March 21 and 31, 2020. Three themes emerged through qualitative content analysis: (1) relational steadiness, (2) navigating COVID-19 challenges in relationships, and (3) relational enhancement.
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Clyde W, Kakolyris A, Koimisis G. A Study of the Effectiveness of Governmental Strategies for Managing Mortality from COVID-19. EASTERN ECONOMIC JOURNAL 2021; 47:487-505. [PMID: 34483381 PMCID: PMC8409076 DOI: 10.1057/s41302-021-00202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We investigate the effectiveness of seven government containment and policy closure interventions against the novel coronavirus (SARS-COV-2) pandemic in the OECD countries, at several different time horizons. Our results indicate that only school closings and public transportation closings have a persistently significant impact. Stay-at-home policies only show a significant impact after 70 days. Workplace closings, restrictions on the size of gatherings, and restrictions on internal travel show no significant impact on mortality rates. Moreover, stricter measures are not significantly associated with lower growth rates in mortality.
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Affiliation(s)
- William Clyde
- Department of Economics & Finance, O’ Malley School of Business, Manhattan College, Riverdale, NY 10471 USA
| | - Andreas Kakolyris
- Department of Economics & Finance, O’ Malley School of Business, Manhattan College, Riverdale, NY 10471 USA
- School of Accounting and Finance, College of Business and Public Management, Kean University, Union, NJ 07083 USA
| | - Georgios Koimisis
- Department of Economics & Finance, O’ Malley School of Business, Manhattan College, Riverdale, NY 10471 USA
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