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Al-Kuwari MG, Mohammed AM, Abdulmajeed J, Al-Romaihi H, Al-Mass M, Abushaikha SS, Albyat S, Nadeem S, Kandy MC. COVID-19 testing, incidence, and positivity trends among school age children during the academic years 2020-2022 in the State of Qatar: special focus on using CDC indicators for community transmission to evaluate school attendance policies and public health response. BMC Pediatr 2024; 24:374. [PMID: 38811909 PMCID: PMC11137921 DOI: 10.1186/s12887-024-04833-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 05/14/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND There exists a gap in our understanding of the age-dependent epidemiological dynamics of SARS-CoV-2 among school-age children in comparison to adults within the State of Qatar. Additionally, there has been limited assessment of the timely implementation of physical distancing interventions, notably national school closures, and their impact on infection trends. METHODS We used the national database to capture all records of polymerase-chain-reaction (PCR) testing, and rapid antigen tests (RAT) conducted at all health care venues in Qatar and administered between August 26, 2020, and August 21, 2022, across all age groups (≥ 5 years old). Study participants under 18 years old were categorized into two age brackets: (5-11) and (12-17), aligning with the Primary and Preparatory/Secondary grade levels in Qatar, respectively. We assessed age group testing rates, incidence rates, and positivity rates in relation to adults. These epidemiological metrics were compared with the CDC's thresholds for COVID-19 community transmission. RESULTS Throughout the school years of 2020-2021 and 2021-2022, a total of 5,063,405 and 6,130,531 tests were respectively conducted. In the 2020-2021 school year, 89.6% of the tests were administered to adults, while 13.7% were conducted on children in the following year. The overall test positivity rates for the 2020-2021 and 2021-2022 school years were 5.8% and 8.1%, respectively. Adolescents underwent the fewest tests during the full study period compared to both adults and young children. Using the CDC indicators, we found that children and adolescents can significantly contribute to elevated infection rates, potentially driving community transmission upon relaxation of social restrictions. CONCLUSION It is crucial to acknowledge the potential for higher transmission among youth and adolescents when formulating transmission control strategies and making decisions regarding school closures. Employing data-driven indicators and thresholds to monitor COVID-19 community levels is important for informing decision-making. These approaches also enable the prompt implementation of infection control transmission mitigation measures in future pandemics.
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Affiliation(s)
- Mohamed Ghaith Al-Kuwari
- Primary Health Care Corporation-Qatar, Corporation, Doha, Qatar
- College of Medicine, Qatar University, Doha, Qatar
| | | | | | | | - Maryam Al-Mass
- Primary Health Care Corporation-Qatar, Corporation, Doha, Qatar
| | | | - Soha Albyat
- Ministry of Public Health- Qatar, Doha, Qatar
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Collin A, Hejblum BP, Vignals C, Lehot L, Thiébaut R, Moireau P, Prague M. Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions. Int J Biostat 2024; 20:13-41. [PMID: 36607837 DOI: 10.1515/ijb-2022-0087] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/08/2022] [Indexed: 01/07/2023]
Abstract
In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.
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Affiliation(s)
- Annabelle Collin
- Inria, Inria Bordeaux - Sud-Ouest, Bordeaux INP, IMB UMR 5251, Université Bordeaux, Talence, France
| | - Boris P Hejblum
- Inria, Inria Bordeaux - Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
- Vaccine Research Institute, F-94000 Créteil, France
| | - Carole Vignals
- Inria, Inria Bordeaux - Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
- Vaccine Research Institute, F-94000 Créteil, France
- CHU Pellegrin, F-33000 Bordeaux, France
| | - Laurent Lehot
- Inria, Inria Bordeaux - Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
- Vaccine Research Institute, F-94000 Créteil, France
| | - Rodolphe Thiébaut
- Inria, Inria Bordeaux - Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
- Vaccine Research Institute, F-94000 Créteil, France
- CHU Pellegrin, F-33000 Bordeaux, France
| | - Philippe Moireau
- ISPED Inserm U1219 Bordeaux Population Health Bureau 23 146 rue Leo Saignat CS 61292 33076 Bordeaux Cedex, France
| | - Mélanie Prague
- Inria, Inria Saclay-Ile de France, France and LMS, CNRS UMR 7649, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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d'Andrea V, Trentini F, Marziano V, Zardini A, Manica M, Guzzetta G, Ajelli M, Petrone D, Del Manso M, Sacco C, Andrianou X, Bella A, Riccardo F, Pezzotti P, Poletti P, Merler S. Spatial spread of COVID-19 during the early pandemic phase in Italy. BMC Infect Dis 2024; 24:450. [PMID: 38684947 PMCID: PMC11057115 DOI: 10.1186/s12879-024-09343-8] [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: 07/19/2023] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.
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Affiliation(s)
- Valeria d'Andrea
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
- Department of Physics and Astronomy "Galileo Galilei", University of Padua, Padua, Italy
| | - Filippo Trentini
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
- Dondena Centre for Research On Social Dynamics and Public Policy, Bocconi University, Milan, Italy
- Department of Decision Sciences, Bocconi University, Milan, Italy
| | | | - Agnese Zardini
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Mattia Manica
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Daniele Petrone
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
- Department of Statistics, Sapienza University of Rome, Rome, Italy
| | - Martina Del Manso
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Chiara Sacco
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Xanthi Andrianou
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Antonino Bella
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Flavia Riccardo
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Piero Poletti
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Stefano Merler
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
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Lison A, Abbott S, Huisman J, Stadler T. Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates. PLoS Comput Biol 2024; 20:e1012021. [PMID: 38626217 PMCID: PMC11051644 DOI: 10.1371/journal.pcbi.1012021] [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: 08/24/2023] [Revised: 04/26/2024] [Accepted: 03/22/2024] [Indexed: 04/18/2024] Open
Abstract
The time-varying effective reproduction number Rt is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of Rt can be obtained from reported cases counted by their date of symptom onset, which is generally closer to the time of infection than the date of report. Case counts by date of symptom onset are typically obtained from line list data, however these data can have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and Rt estimation into a single generative Bayesian model, allowing direct joint inference of case counts and Rt from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different epidemic phases. We find that under reporting delays realistic for hospitalization data (50% of reports delayed by more than a week), intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and Rt, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. The generative modeling components developed in this work have been integrated and further extended in the R package epinowcast, providing a flexible and interpretable tool for real-time surveillance.
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Affiliation(s)
- Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jana Huisman
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Muhsen K, Cohen D, Glatman-Freedman A, Husseini S, Perlman S, McNeil C. Review of Israel's action and response during the COVID-19 pandemic and tabletop exercise for the evaluation of readiness and resilience-lessons learned 2020-2021. Front Public Health 2024; 11:1308267. [PMID: 38328537 PMCID: PMC10847317 DOI: 10.3389/fpubh.2023.1308267] [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: 10/06/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background Reevaluating response plans is essential to ensuring consistent readiness and resilience to the COVID-19 pandemic. The "During Action Review" and Tabletop (DART) methodology provides a retrospective and prospective assessment to inform the adaptive response. Israel introduced COVID-19 vaccinations in December 2020 and was the first country to implement booster vaccination to address waning immunity and surges caused by new variants. We assessed Israel's readiness and resilience related to COVID-19 response while capturing the pre-vaccination and vaccination periods. Methods A DART analysis was conducted between December 2020 and August 2021 among experts involved in the management of the COVID-19 pandemic in Israel. During the retrospective stage, a role-based questionnaire and discussions were undertaken in a participant-led review of the response, focusing on epidemiology and surveillance, risk communication, and vaccines. The prospective stage included tabletop exercises to evaluate short to long-term simulated scenarios. Results Participants emphasized the pivotal role of Israel globally by sharing experiences with the pandemic, and vaccination. Perceived strengths included multi-sectoral collaboration between the Ministry of Health, healthcare providers, academia, military, and others, stretching capacities, expanding laboratory workload, and establishing/maintaining surveillance. The vaccine prioritization plan and strong infrastructure, including computerized databases, enabled real-life assessment of vaccine uptake and impact. Challenges included the need to change case definitions early on and insufficient staffing. Quarantine of patients and contacts was particularly challenging among underprivileged communities. Risk communication approaches need to focus more on creating norms in behavior. Trust issues and limited cooperation were noted, especially among ethnic and religious minorities. To ensure readiness and resiliency, participants recommended establishing a nationally deployed system for bringing in and acting upon feedback from the field, especially concerning risk communication and vaccines. Conclusion Our study appraised strengths and weaknesses of the COVID-19 pandemic response in Israel and led to concrete recommendations for adjusting responses and future similar events. An efficient response comprised multi-sectoral collaboration, policy design, infrastructure, care delivery, and mitigation measures, including vaccines, while risk communication, trust issues, and limited cooperation with minority groups were perceived as areas for action and intervention.
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Affiliation(s)
- Khitam Muhsen
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Middle East Consortium on Infectious Disease Surveillance, Jerusalem, Israel
| | - Dani Cohen
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Middle East Consortium on Infectious Disease Surveillance, Jerusalem, Israel
| | - Aharona Glatman-Freedman
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Israel Center for Disease Control, Israel Ministry of Health, Ramat Gan, Israel
| | - Sari Husseini
- Middle East Consortium on Infectious Disease Surveillance, Jerusalem, Israel
| | - Saritte Perlman
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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6
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Cao H, Cao L. Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19. Sci Rep 2024; 14:707. [PMID: 38184669 PMCID: PMC10771507 DOI: 10.1038/s41598-023-50421-9] [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: 10/21/2023] [Accepted: 12/19/2023] [Indexed: 01/08/2024] Open
Abstract
As COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a "green pass", to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD[Formula: see text] to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD[Formula: see text] also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual's freedom during the pandemic.
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Affiliation(s)
- Hu Cao
- School of Computing, Macquarie University, Sydney, NSW, 2109, Australia
| | - Longbing Cao
- School of Computing, Macquarie University, Sydney, NSW, 2109, Australia.
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7
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Rehms R, Ellenbach N, Rehfuess E, Burns J, Mansmann U, Hoffmann S. A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19. Biom J 2024; 66:e2200341. [PMID: 38285407 DOI: 10.1002/bimj.202200341] [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: 12/05/2022] [Revised: 08/21/2023] [Accepted: 08/24/2023] [Indexed: 01/30/2024]
Abstract
Infectious disease models can serve as critical tools to predict the development of cases and associated healthcare demand and to determine the set of nonpharmaceutical interventions (NPIs) that is most effective in slowing the spread of an infectious agent. Current approaches to estimate NPI effects typically focus on relatively short time periods and either on the number of reported cases, deaths, intensive care occupancy, or hospital occupancy as a single indicator of disease transmission. In this work, we propose a Bayesian hierarchical model that integrates multiple outcomes and complementary sources of information in the estimation of the true and unknown number of infections while accounting for time-varying underreporting and weekday-specific delays in reported cases and deaths, allowing us to estimate the number of infections on a daily basis rather than having to smooth the data. To address dynamic changes occurring over long periods of time, we account for the spread of new variants, seasonality, and time-varying differences in host susceptibility. We implement a Markov chain Monte Carlo algorithm to conduct Bayesian inference and illustrate the proposed approach with data on COVID-19 from 20 European countries. The approach shows good performance on simulated data and produces posterior predictions that show a good fit to reported cases, deaths, hospital, and intensive care occupancy.
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Affiliation(s)
- Raphael Rehms
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Nicole Ellenbach
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Eva Rehfuess
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Jacob Burns
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Ulrich Mansmann
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Sabine Hoffmann
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-University Munich, Munich, Germany
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
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Lim TY, Xu R, Ruktanonchai N, Saucedo O, Childs LM, Jalali MS, Rahmandad H, Ghaffarzadegan N. Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates. Health Aff (Millwood) 2023; 42:1637-1646. [PMID: 38048504 DOI: 10.1377/hlthaff.2023.00713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission. We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.
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Affiliation(s)
- Tse Yang Lim
- Tse Yang Lim, Harvard University, Boston, Massachusetts
| | - Ran Xu
- Ran Xu, University of Connecticut, Storrs, Connecticut
| | | | - Omar Saucedo
- Omar Saucedo, Virginia Tech, Blacksburg, Virginia
| | | | | | - Hazhir Rahmandad
- Hazhir Rahmandad, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Hanratty J, Keenan C, O'Connor SR, Leonard R, Chi Y, Ferguson J, Axiaq A, Miller S, Bradley D, Dempster M. Psychological and psychosocial determinants of COVID Health Related Behaviours (COHeRe): An evidence and gap map. CAMPBELL SYSTEMATIC REVIEWS 2023; 19:e1336. [PMID: 37361553 PMCID: PMC10286725 DOI: 10.1002/cl2.1336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Background The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. Common recommended measures to limit risk of infection include frequent handwashing, reducing the frequency of social interactions and the use of face coverings. It is important to identify those factors that can predict the uptake and maintenance of these protective behaviours. Objectives We aimed to identify and map the existing evidence (published and unpublished) on psychological and psychosocial factors that determine uptake and adherence to behaviours aimed at reducing the risk of infection or transmission of COVID-19. Search Methods Our extensive search included electronic databases (n = 12), web searches, conference proceedings, government reports, other repositories including both published peer reviewed, pre-prints and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID-19), (2) behaviours of interest and (3) terms related to psychological and psychosocial determinants of COVID Health-Related Behaviours and adherence or compliance with recommended behaviours, to capture both malleable and non-malleable determinants (i.e. determinants that could be changed and those that could not). Selection Criteria This Evidence and Gap Map (EGM) includes all types of studies examining determinants of common recommended behaviours aimed at mitigating human-to-human spread of COVID-19. All potential malleable and non-malleable determinants of one or more behaviours are included in the map. As part of the mapping process, categories are used to group determinants. The mapping categories were based on a previous rapid review by Hanratty 2021. These include: 'behaviour', 'cognition', 'demographics', 'disease', 'emotions', 'health status', 'information', 'intervention', and 'knowledge'. Those not suitable for categorisation in any of these groups are included in the map as 'other' determinants. Data Collection and Analysis Results were imported to a bibliographic reference manager where duplications of identical studies gathered from multiple sources were removed. Data extraction procedures were managed in EPPI-Reviewer software. Information on study type, population, behaviours measured and determinants measured were extracted. We appraised the methodological quality of systematic reviews with AMSTAR-2. We did not appraise the quality of primary studies in this map. Main Results As of 1 June 2022 the EGM includes 1034 records reporting on 860 cross-sectional, 68 longitudinal, 78 qualitative, 25 reviews, 62 interventional, and 39 other studies (e.g., mixed-methods approaches). The map includes studies that measured social distancing (n = 487), masks and face coverings (n = 382), handwashing (n = 308), physical distancing (n = 177), isolation/quarantine (n = 157), respiratory hygiene/etiquette (n = 75), cleaning surfaces (n = 59), and avoiding touching the T-zone (n = 48). There were 333 studies that assessed composite measures of two or more behaviours. The largest cluster of determinants was 'demographics' (n = 730 studies), followed by 'cognition' (n = 496 studies) and determinants categorised as 'other' (n = 447). These included factors such as 'beliefs', 'culture' and 'access to resources'. Less evidence is available for some determinants such as 'interventions' (n = 99 studies), 'information' (n = 101 studies), and 'behaviour' (149 studies). Authors' Conclusions This EGM provides a valuable resource for researchers, policy-makers and the public to access the available evidence on the determinants of various COVID-19 health-related behaviours. The map can also be used to help guide research commissioning, by evidence synthesis teams and evidence intermediaries to inform policy during the ongoing pandemic and potential future outbreaks of COVID-19 or other respiratory infections. Evidence included in the map will be explored further through a series of systematic reviews examining the strength of the associations between malleable determinants and the uptake and maintenance of individual protective behaviours.
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Affiliation(s)
- Jennifer Hanratty
- School of PsychologyQueen's University BelfastBelfastUK
- Centre for Effective ServicesBelfastUK
| | | | | | | | - Yuan Chi
- Cochrane Global AgeingShanghaiChina
| | - Janet Ferguson
- School of PsychologyQueen's University BelfastBelfastUK
- Applied Behaviour Research ClinicUniversity of GalwayGalwayIreland
| | - Ariana Axiaq
- School of PsychologyQueen's University BelfastBelfastUK
| | - Sarah Miller
- School of Education, Social Sciences and Social WorkQueen's University BelfastBelfastUK
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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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Etemad K, Mohseni P, Shojaei S, Mousavi SA, Taherkhani S, Fallah Atatalab F, Ghajari H, Hashemi Nazari SS, Karami M, Izadi N, Hajipour M. Non-Pharmacologic Interventions in COVID-19 Pandemic Management; a Systematic Review. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2023; 11:e52. [PMID: 37671267 PMCID: PMC10475751 DOI: 10.22037/aaem.v11i1.1828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Introduction Different countries throughout the world have adopted non-pharmacologic interventions to reduce and control SARS - CoV-2. In this systematic approach, the impact of non-pharmacologic interventions in management of COVID-19 pandemic was assessed. Methods Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, systematic search was carried out on the basis of a search strategy on PubMed, Web of Science, Scopus, and WHO databases on COVID-19. The impact of travel ban, personal protective equipment, distancing, contact tracing, school closure, and social distancing and the combined effect of interventions on COVID-19 were assessed. Results Of the 14,857 articles found, 44 were relevant. Studies in different countries have shown that various non-pharmacological interventions have been used during the COVID-19 pandemic. The travel ban, either locally or internationally in most of the countries, movement restriction, social distancing, lockdown, Personal Protective Equipment (PPE), quarantine, school closure, work place closure, and contact tracing had a significant impact on the reduction of mortality or morbidity of COVID-19. Conclusion Evidence shows that the implementation of non-pharmacologic interventions (NPIs), for this study suggests that the effectiveness of any NPI alone is probably limited, thus, a combination of various actions, for example, social distancing, isolation, and quarantine, distancing in the workplace and use of personal protective equipment, is more effective in reducing COVID-19.
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Affiliation(s)
- Koorosh Etemad
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Mohseni
- Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Saeideh Shojaei
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mousavi
- Department of Public Health, Shoushtar Faculty of Medical Science, Shoushtar, Iran
| | - Shakiba Taherkhani
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Fallah Atatalab
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hadis Ghajari
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Saeed Hashemi Nazari
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Manoochehr Karami
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Neda Izadi
- Department of Epidemiology, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahmoud Hajipour
- Pediatric Gastroenterology, Hepatology and Nutrition Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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12
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Yang L, Hu M, Zeng H, Liang W, Zhu J. The impact of multiple non-pharmaceutical interventions for China-bound travel on domestic COVID-19 outbreaks. Front Public Health 2023; 11:1202996. [PMID: 37521963 PMCID: PMC10373927 DOI: 10.3389/fpubh.2023.1202996] [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: 04/10/2023] [Accepted: 06/01/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives Non-pharmaceutical interventions (NPIs) implemented on China-bound travel have successfully mitigated cross-regional transmission of COVID-19 but made the country face ripple effects. Thus, adjusting these interventions to reduce interruptions to individuals' daily life while minimizing transmission risk was urgent. Methods An improved Susceptible-Infected-Recovered (SIR) model was built to evaluate the Delta variant's epidemiological characteristics and the impact of NPIs. To explore the risk associated with inbound travelers and the occurrence of domestic traceable outbreaks, we developed an association parameter that combined inbound traveler counts with a time-varying initial value. In addition, multiple time-varying functions were used to model changes in the implementation of NPIs. Related parameters of functions were run by the MCSS method with 1,000 iterations to derive the probability distribution. Initial values, estimated parameters, and corresponding 95% CI were obtained. Reported existing symptomatic, suspected, and asymptomatic case counts were used as the training datasets. Reported cumulative recovered individual data were used to verify the reliability of relevant parameters. Lastly, we used the value of the ratio (Bias2/Variance) to verify the stability of the mathematical model, and the effects of the NPIs on the infected cases to analyze the sensitivity of input parameters. Results The quantitative findings indicated that this improved model was highly compatible with publicly reported data collected from July 21 to August 30, 2021. The number of inbound travelers was associated with the occurrence of domestic outbreaks. A proportional relationship between the Delta variant incubation period and PCR test validity period was found. The model also predicted that restoration of pre-pandemic travel schedules while adhering to NPIs requirements would cause shortages in health resources. The maximum demand for hospital beds would reach 25,000/day, the volume of PCR tests would be 8,000/day, and the number of isolation rooms would reach 800,000/day within 30 days. Conclusion With the pandemic approaching the end, reexamining it carefully helps better address future outbreaks. This predictive model has provided scientific evidence for NPIs' effectiveness and quantifiable evidence of health resource allocation. It could guide the design of future epidemic prevention and control policies, and provide strategic recommendations on scarce health resource allocation.
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Affiliation(s)
- Lichao Yang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Mengzhi Hu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Jiming Zhu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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13
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Mendes JM, Coelho PS. The effect of non-pharmaceutical interventions on COVID-19 outcomes: A heterogeneous age-related generalisation of the SEIR model. Infect Dis Model 2023; 8:S2468-0427(23)00044-1. [PMID: 37366483 PMCID: PMC10287188 DOI: 10.1016/j.idm.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 05/26/2023] [Accepted: 05/29/2023] [Indexed: 06/28/2023] Open
Abstract
Successive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic and the assessment of the public health measures adopted and named under the common umbrella of Non-Pharmaceutical Interventions (NPIs). So far, these generalisations have not been able to assess the ability of these measures to avoid infection by the SARS-CoV-2 and thus their contribution to contain the spread of the disease. This work proposes a new generalisation of SEIR model and includes a heterogeneous and age-related generation of infections that depends both on a probability that a contact generates the transmission of the disease and a contact rate. The results show (1) thanks to the universal wearing of facial coverings, the probability that a contact provokes the transmission of the disease was reduced by at least 50% and (2) the impact of the other NPI is so significant that otherwise Portugal would have gone into a non-sustainable situation of having 80% of its population infected in the first 300 days of the pandemic. This situation would have led to a number of deaths almost twenty times higher than the number that was actually recorded by December 26th, 2020. Moreover, the results suggest that even if the requirement of universal wearing of facial coverings was adopted sooner jointly with closing workplaces and resorting to teleworking would have postponed the peak of the incidence, altought the epidemic path would have result in a number of infections hardly managed by the National Health System. Complementary, results confirm that (3) the health authorities adopted a conservative approach on the criteria to consider an infected individual not infective any longer; and (4) the most effective NPIs and stringency levels either impacting on self-protection against infection or reducing the contacts that would eventually result in infection are, in decreasing order of importance, the use of Facial coverings, Workplace closing and Stay at home requirements.
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Affiliation(s)
- Jorge M. Mendes
- NOVA Information Management School (NOVAIMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
- NOVA Cairo at the Knowledge Hub Universities, New Admnistrative Capital, Cairo, Egypt
| | - Pedro S. Coelho
- NOVA Information Management School (NOVAIMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
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14
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Peters JA, Farhadloo M. The Effects of Non-Pharmaceutical Interventions on COVID-19 Cases, Hospitalizations, and Mortality: A Systematic Literature Review and Meta-Analysis. AJPM FOCUS 2023; 2:100125. [PMID: 37362389 PMCID: PMC10265928 DOI: 10.1016/j.focus.2023.100125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Introduction To assess the effects of various non-pharmaceutical interventions (NPI) on cases, hospitalizations, and mortality during the first wave of the COVID-19 pandemic. Methods To empirically investigate the impacts of different NPIs on COVID-19-related health outcomes, a systematic literature review was conducted. We studied the effects of 10 NPIs on cases, hospitalizations, and mortality across three periodic lags (2, 3, and 4 weeks-or-more following implementation). Articles measuring the impact of NPIs were sourced from three databases by May 10, 2022, and risk of bias was assessed using the Newcastle-Ottawa scale. Results Across the 44 papers, we found that mask wearing corresponded to decreased per capita cases across all lags (up to -2.71 per 100,000). All NPIs studied except business and bar/restaurant closures corresponded to reduced case growth rates in the two weeks following implementation, while policy stringency and travelling restrictions were most effective after four. While we did not find evidence of reduced deaths in our per capita estimates, policy stringency, masks, SIPOs, limited gatherings, school and business closures were associated with decreased mortality growth rates. Moreover, the two NPIs studied in hospitalizations (SIPOs and mask wearing) showed negative estimates. Conclusions When assessing the impact of NPIs, considering the duration of effectiveness following implementation has paramount significance. While some NPIs may reduce the COVID-19 impact, others can disrupt the mitigative progression of containing the virus. Policymakers should be aware of both the scale of their effectiveness and duration of impact when adopting these measures for future COVID-19 waves.
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Affiliation(s)
- James A. Peters
- Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Quebec, Canada
| | - Mohsen Farhadloo
- Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Quebec, Canada
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15
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Ma Y, Wang H, Huang Y, Chen C, Liang S, Ma M, He X, Cai K, Jiao Z, Chen L, Zhu B, Li K, Xie C, Luo L, Zhang Z. The Role of "Hierarchical and Classified Prevention and Control Measures (HCPC)" Strategy for SARS-CoV-2 Delta Variant in Guangzhou: A Modeling Study. J Epidemiol Glob Health 2023; 13:303-312. [PMID: 37258853 PMCID: PMC10231852 DOI: 10.1007/s44197-023-00108-1] [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: 08/11/2022] [Accepted: 04/04/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND The Delta variant of SARS-COV-2 has replaced previously circulating strains around the world in 2021. Sporadic outbreaks of the Delta variant in China have posed a concern about how to properly respond to the battle against evolving COVID-19. Here, we analyzed the "hierarchical and classified prevention and control (HCPC)" measures strategy deployed during the recent Guangzhou outbreak. METHODS A modified susceptible-exposed-pre-symptomatic-infectious-recovered (SEPIR) model was developed and applied to study a range of different scenarios to evaluate the effectiveness of policy deployment. We simulated severe different scenarios to understand policy implementation and timing of implementation. Two outcomes were measured: magnitude of transmission and duration of transmission. The outcomes of scenario evaluations were presented relative to the reality case (i.e., 368 cases in 34 days) with 95% confidence interval (CI). RESULTS Based on our simulation, the outbreak would become out of control with 7 million estimated infections under the assumption of the absence of any interventions than the 153 reported cases in reality in Guangzhou. The simulation on delayed implementation of interventions showed that the total case numbers would also increase by 166.67%-813.07% if the interventions were delayed by 3 days or 7 days. CONCLUSIONS It may be concluded that timely and more precise interventions including mass testing and graded community management are effective measures for Delta variant containment in China.
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Affiliation(s)
- Yu Ma
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Hui Wang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Yong Huang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Chun Chen
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Shihao Liang
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Mengmeng Ma
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Xinjun He
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Kangning Cai
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Zengtao Jiao
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Liyi Chen
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Bowei Zhu
- Yidu Cloud (Beijing) Technology Co.Ltd, Beijing, 100083, People's Republic of China
| | - Ke Li
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Chaojun Xie
- Guangzhou Huadu District Center for Disease Control and Prevention, Guangzhou, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China.
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China.
| | - Zhoubin Zhang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China.
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, 510120, People's Republic of China.
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16
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Sen A, Baker JD, Zhang Q, Agarwal RR, Lam JP. Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces. ECONOMIC ANALYSIS AND POLICY 2023; 78:225-242. [PMID: 36941918 PMCID: PMC9993801 DOI: 10.1016/j.eap.2023.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 01/21/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible-Infected-Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution.
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Affiliation(s)
- Anindya Sen
- Department of Economics, University of Waterloo, 200 University Avenue W., Waterloo, Ontario, Canada N2L 3G1
| | - John David Baker
- Department of Economics, University of Waterloo, 200 University Avenue W., Waterloo, Ontario, Canada N2L 3G1
| | - Qihuang Zhang
- Department of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104-6021, United States of America
| | - Rishav Raj Agarwal
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
| | - Jean-Paul Lam
- Department of Economics, University of Waterloo, 200 University Avenue W., Waterloo, Ontario, Canada N2L 3G1
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17
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Porcu G, Chen YX, Bonaugurio AS, Villa S, Riva L, Messina V, Bagarella G, Maistrello M, Leoni O, Cereda D, Matone F, Gori A, Corrao G. Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends. Front Public Health 2023; 11:1141688. [PMID: 37275497 PMCID: PMC10233021 DOI: 10.3389/fpubh.2023.1141688] [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: 01/11/2023] [Accepted: 04/28/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.
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Affiliation(s)
- Gloria Porcu
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
| | - Yu Xi Chen
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Andrea Stella Bonaugurio
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Simone Villa
- Centre for Multidisciplinary Research in Health Science, University of Milan, Milan, Italy
| | - Leonardo Riva
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
- PoliS Lombardia, Milan, Italy
| | - Vincenzina Messina
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
- PoliS Lombardia, Milan, Italy
| | - Giorgio Bagarella
- Directorate General for Health, Lombardy Region, Milan, Italy
- Agency for Health Protection of the Metropolitan Area of Milan, Lombardy Region, Milan, Italy
| | - Mauro Maistrello
- Directorate General for Health, Lombardy Region, Milan, Italy
- Local Health Unit of Melegnano and Martesana, Milan, Italy
| | - Olivia Leoni
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Danilo Cereda
- Directorate General for Health, Lombardy Region, Milan, Italy
| | | | - Andrea Gori
- ASST Fatebenefratelli-Sacco, Luigi Sacco Hospital – University of Milan, Milan, Italy
- Department of Pathophysiology and Transplantation, School of Medicine and Surgery, University of Milan, Milan, Italy
| | - Giovanni Corrao
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
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Gallant AJ, Harding A, Johnson C, Steenbeek A, Curran JA. Identifying H1N1 and COVID-19 vaccine hesitancy or refusal among health care providers: a scoping review. JBI Evid Synth 2023; 21:913-951. [PMID: 36917102 PMCID: PMC10173945 DOI: 10.11124/jbies-22-00112] [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] [Indexed: 03/15/2023]
Abstract
OBJECTIVES The objective of this review was to describe and map the evidence on COVID-19 and H1N1 vaccine hesitancy or refusal by physicians, nurses, and pharmacists in North America, the United Kingdom and the European Union, and Australia. INTRODUCTION Since 2009, we have experienced two pandemics: H1N1 "swine flu" and COVID-19. While severity and transmissibility of these viruses varied, vaccination has been a critical component of bringing both pandemics under control. However, uptake of these vaccines has been affected by vaccine hesitancy and refusal. The vaccination behaviors of health care providers, including physicians, nurses, and pharmacists, are of particular interest as they have been priority populations to receive both H1N1 and COVID-19 vaccinations. Their vaccination views could affect the vaccination decisions of their patients. INCLUSION CRITERIA Studies were eligible for inclusion if they identified reasons for COVID-19 or H1N1 vaccine hesitancy or refusal among physicians, nurses, or pharmacists from the included countries. Published and unpublished literature were eligible for inclusion. Previous reviews were excluded; however, the reference lists of relevant reviews were searched to identify additional studies for inclusion. METHODS A search of CINAHL, MEDLINE, PsycINFO, and Academic Search Premier databases was conducted April 28, 2021, to identify English-language literature published from 2009 to 2021. Gray literature and citation screening were also conducted to identify additional relevant literature. Titles, abstracts, and eligible full-text articles were reviewed in duplicate by 2 trained reviewers. Data were extracted in duplicate using a structured extraction tool developed for the review. Conflicts were resolved through discussion or with a third team member. Data were synthesized using narrative and tabular summaries. RESULTS In total, 83 articles were included in the review. Studies were conducted primarily across the United States, the United Kingdom, and France. The majority of articles (n=70) used cross-sectional designs to examine knowledge, attitudes, and uptake of H1N1 (n=61) or COVID-19 (n=22) vaccines. Physicians, medical students, nurses, and nursing students were common participants in the studies; however, only 8 studies included pharmacists in their sample. Across health care settings, most studies were conducted in urban, academic teaching hospitals, with 1 study conducted in a rural hospital setting. Concerns about vaccine safety, vaccine side effects, and perceived low risk of contracting H1N1 or COVID-19 were the most common reasons for vaccine hesitancy or refusal across both vaccines. CONCLUSIONS With increased interest and attention on vaccines in recent years, intensified by the COVID-19 pandemic, more research that examines vaccine hesitancy or refusal across different health care settings and health care providers is warranted. Future work should aim to utilize more qualitative and mixed methods research designs to capture the personal perspectives of vaccine hesitancy and refusal, and consider collecting data beyond the common urban and academic health care settings identified in this review.
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Affiliation(s)
| | | | | | | | - Janet A. Curran
- IWK Health Centre, Halifax, NS, Canada
- School of Nursing, Dalhousie University, Halifax, NS, Canada
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19
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Banholzer N, Zürcher K, Jent P, Bittel P, Furrer L, Egger M, Hascher T, Fenner L. SARS-CoV-2 transmission with and without mask wearing or air cleaners in schools in Switzerland: A modeling study of epidemiological, environmental, and molecular data. PLoS Med 2023; 20:e1004226. [PMID: 37200241 PMCID: PMC10194935 DOI: 10.1371/journal.pmed.1004226] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 03/28/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Growing evidence suggests an important contribution of airborne transmission to the overall spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), in particular via smaller particles called aerosols. However, the contribution of school children to SARS-CoV-2 transmission remains uncertain. The aim of this study was to assess transmission of airborne respiratory infections and the association with infection control measures in schools using a multiple-measurement approach. METHODS AND FINDINGS We collected epidemiological (cases of Coronavirus Disease 2019 (COVID-19)), environmental (CO2, aerosol and particle concentrations), and molecular data (bioaerosol and saliva samples) over 7 weeks from January to March 2022 (Omicron wave) in 2 secondary schools (n = 90, average 18 students/classroom) in Switzerland. We analyzed changes in environmental and molecular characteristics between different study conditions (no intervention, mask wearing, air cleaners). Analyses of environmental changes were adjusted for different ventilation, the number of students in class, school and weekday effects. We modeled disease transmission using a semi-mechanistic Bayesian hierarchical model, adjusting for absent students and community transmission. Molecular analysis of saliva (21/262 positive) and airborne samples (10/130) detected SARS-CoV-2 throughout the study (weekly average viral concentration 0.6 copies/L) and occasionally other respiratory viruses. Overall daily average CO2 levels were 1,064 ± 232 ppm (± standard deviation). Daily average aerosol number concentrations without interventions were 177 ± 109 1/cm3 and decreased by 69% (95% CrI 42% to 86%) with mask mandates and 39% (95% CrI 4% to 69%) with air cleaners. Compared to no intervention, the transmission risk was lower with mask mandates (adjusted odds ratio 0.19, 95% CrI 0.09 to 0.38) and comparable with air cleaners (1.00, 95% CrI 0.15 to 6.51). Study limitations include possible confounding by period as the number of susceptible students declined over time. Furthermore, airborne detection of pathogens document exposure but not necessarily transmission. CONCLUSIONS Molecular detection of airborne and human SARS-CoV-2 indicated sustained transmission in schools. Mask mandates were associated with greater reductions in aerosol concentrations than air cleaners and with lower transmission. Our multiple-measurement approach could be used to continuously monitor transmission risk of respiratory infections and the effectiveness of infection control measures in schools and other congregate settings.
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Affiliation(s)
- Nicolas Banholzer
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Kathrin Zürcher
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Philipp Jent
- Department of Infectious Diseases, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pascal Bittel
- Institute for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Lavinia Furrer
- Institute for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tina Hascher
- Institute of Educational Science, University of Bern, Bern, Switzerland
| | - Lukas Fenner
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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20
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Dam D, McGill E, Bellos A, Coulby C, Edwin J, McCormick R, Patterson K. COVID-19 outbreak trends in Canada, 2021. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2023; 49:133-144. [PMID: 38385104 PMCID: PMC10881080 DOI: 10.14745/ccdr.v49i04a06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Background In January 2021, the Public Health Agency of Canada launched an outbreak surveillance system, the Canadian COVID-19 Outbreak Surveillance System (CCOSS), with the goal of monitoring incidence and severity of coronavirus disease 2019 (COVID-19) outbreaks across various community settings and complementing case surveillance. Methods Seven provinces were included in this report; these provinces submitted weekly cumulative COVID-19 outbreak line lists to CCOSS in 2021. Data includes administrative variables (e.g. date outbreak declared, date outbreak declared over, outbreak identifier), 24 outbreak settings, and number of confirmed cases and outcomes (hospitalization, death). Descriptive analyses for COVID-19 outbreaks across Canada from January 3, 2021, to January 1, 2022, were performed examining trends over time, severity, and outbreak size. Results Incidence of outbreaks followed similar trends to case incidence. Outbreaks were most common in school and childcare settings (39%) and industrial/agricultural settings (21%). Outbreak size ranged from 2 to 639 cases per outbreak; the median size was four cases per outbreak. Correctional facilities had the largest median outbreak size with 18 cases per outbreak, followed by long-term care facilities with 10 cases per outbreak. During periods of high case incidence, outbreaks may be under-ascertained due to limited public health capacity, or reporting may be biased towards high-risk settings prioritized for testing. Outbreaks reported to CCOSS were dominated by jurisdictions with the largest populations. Conclusion The trends illustrate that COVID-19 outbreaks in 2021 were reported most frequently in community settings such as schools; however, the largest outbreaks occurred in congregate living settings. The information gathered from outbreak surveillance complemented case incidence trends and furthered understanding of COVID-19 in Canada.
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Affiliation(s)
- Demy Dam
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Erin McGill
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Anna Bellos
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Cameron Coulby
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Jonathan Edwin
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Rachel McCormick
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Kaitlin Patterson
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
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21
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Lison A, Banholzer N, Sharma M, Mindermann S, Unwin HJT, Mishra S, Stadler T, Bhatt S, Ferguson NM, Brauner J, Vach W. Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic. Lancet Public Health 2023; 8:e311-e317. [PMID: 36965985 PMCID: PMC10036127 DOI: 10.1016/s2468-2667(23)00046-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 03/27/2023]
Abstract
Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based choices of NPIs during subsequent waves or future epidemics. However, methodological issues and no standardised assessment practices have restricted the practical value of the existing evidence. Here, we present and discuss lessons learned from the COVID-19 pandemic and make recommendations for standardising and improving assessment, data collection, and modelling. These recommendations could contribute to reliable and policy-relevant assessments of the effectiveness of NPIs during future epidemics.
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Affiliation(s)
- Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Nicolas Banholzer
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, Oxford, UK; Future of Humanity Institute, University of Oxford, Oxford, UK
| | - Sören Mindermann
- Department of Computer Science, University of Oxford, Oxford, UK
| | - H Juliette T Unwin
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Swapnil Mishra
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Samir Bhatt
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK; Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Neil M Ferguson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Jan Brauner
- Department of Computer Science, University of Oxford, Oxford, UK; Future of Humanity Institute, University of Oxford, Oxford, UK
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland; Department of Environmental Sciences, University of Basel, Basel, Switzerland
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Gurbuz O, Aldrete RM, Salgado D, Gurbuz TM. Transportation as a Disease Vector in COVID-19: Border Mobility and Disease Spread. TRANSPORTATION RESEARCH RECORD 2023; 2677:826-838. [PMID: 38602941 PMCID: PMC10008995 DOI: 10.1177/03611981231156588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
More than a year after COVID-19 was declared a pandemic by the World Health Organization, the U.S.A. and Mexico rank first and fourth, respectively, with regard to the number of deaths. From March 2020, nonessential travelers were not allowed to cross the border into the U.S.A. from Mexico via international land ports of entry, which resulted in a more than 50% decrease in the number of people crossing the border. However, border communities still face a higher number of cases and faster community spread compared with those without international land ports of entry. This paper established an econometric model to understand the effects of cross-border mobility and other socioeconomic parameters on the speed of spread. The model was developed at the U.S. county level using data from all 3,141 counties in the U.S.A. Additionally, a follow-up U.S. county comparative analysis was developed to examine the significance of having a border crossing between the U.S.A. and Mexico for U.S. counties. The findings of the analysis revealed that the variables having a significant effect are as follows: population density; number of people per household; population in the 15-65 age group; median household income; mask use; number of visits to transit stations; number of visits to workplace; overall mobility; and having a border crossing to Mexico within county limits. The comparative analysis found that U.S. counties with border crossings have an average of 123 cases per 1,000 population whereas their counterparts without border crossings only have 90 cases per 1,000 population.
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Affiliation(s)
- Okan Gurbuz
- Texas A&M Transportation Institute, El Paso, TX
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23
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Huang HN, Xie T, Chen WF, Wei YY. Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics. ZEITSCHRIFT FUR GESUNDHEITSWISSENSCHAFTEN = JOURNAL OF PUBLIC HEALTH 2023:1-12. [PMID: 36844446 PMCID: PMC9942014 DOI: 10.1007/s10389-023-01843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/02/2023] [Indexed: 02/23/2023]
Abstract
Aim Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making. Subject and methods Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics. Results The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds. Conclusion The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts. Supplementary Information The online version contains supplementary material available at 10.1007/s10389-023-01843-2.
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Affiliation(s)
- Hai-nan Huang
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
- School of Management, Jinan University, Guangzhou, 510632 China
| | - Tian Xie
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
| | - Wei-fan Chen
- Information Sciences and Technology, The Pennsylvania State University, State College, PA 16802 USA
| | - Yao-yao Wei
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
- School of Education, Central China Normal University, Wuhan, 430079 China
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24
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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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25
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Heck CJ, Theodore DA, Sovic B, Austin E, Yang C, Rotbert J, Greissman S, Zucker J, Autry A, Catallozzi M, Sobieszczyk ME, Castor D. Correlates of psychological distress among undergraduate women engaged in remote learning through a New York City college during the COVID-19 pandemic. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023:1-10. [PMID: 36649543 PMCID: PMC10350472 DOI: 10.1080/07448481.2022.2156797] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/14/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE The study's objective is to explore psychological distress (PD) among remote learners during COVID-19. PARTICIPANTS Female undergraduates matriculated at an NYC college in Winter 2020. METHODS Using the Kessler-6 scale, we defined PD as no/low (LPD), mild/moderate (MPD), and severe (SPD) and assessed if residing in/near NYC modified associations. RESULTS PD was common (MPD: 34.1%, SPD: 38.9%). Students identifying as Other/Multiracial had lower MPD odds (aOR = 0.39 [0.17-0.88]). SPD was associated with identifying as White (aOR = 2.02 [1.02-3.99]), unbalanced meals (aOR = 2.59 [1.06-6.30]), violence experience (aOR = 1.77 [1.06-2.94]), no social support (aOR = 3.24 [1.37-7.64]), and loneliness (aOR = 2.52 [1.29-4.95]). Among students in/near NYC, moderate/high drug use (aOR = 2.76 [1.15-6.61]), no social support (aOR = 3.62 [1.10-1.19]), and loneliness (aOR = 2.92 [1.11-7.63]) were SPD correlates. CONCLUSIONS PD was high and associated with food insecurity, violence experience, no social support, and loneliness. Living in/near NYC modified drug use, loneliness, and social support associations. Mental health initiatives should address modifiable risk factors to ameliorate pandemic-associated PD.
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Affiliation(s)
- Craig J. Heck
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY
| | - Deborah A. Theodore
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Brit Sovic
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Eloise Austin
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | | | - Samantha Greissman
- Department of Medicine, NewYork-Presbyterian/Columbia University Medical Center, New York, NY
| | - Jason Zucker
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | - Marina Catallozzi
- Barnard College, New York, NY
- Division of Child and Adolescent Health, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY
- Heilbrunn Department of Population & Family Health, Columbia University Mailman School of Public Health, New York, NY
| | - Magdalena E. Sobieszczyk
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Delivette Castor
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY
- Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY
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Mitze T, Makkonen T. Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023; 186:122073. [PMID: 36404872 PMCID: PMC9650712 DOI: 10.1016/j.techfore.2022.122073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 08/23/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
The COVID-19 pandemic and subsequent public health restrictions led to a significant slump in economic activities around the globe. This slump has been met by various policy actions to cushion the detrimental socio-economic consequences of the COVID-19 crisis and eventually bring the economy back on track. We provide an ex-ante evaluation of the effectiveness of a massive expansion of RDI funding in Finland to stimulate post-crisis recovery growth through an increase in RDI activities of Finnish firms. We make use of the fact that novel RDI grants for firms in disruptive circumstances granted in 2020 were allocated through established RDI policy channels. This allows us to estimate the structural link between RDI funding and economic growth for Finnish NUTS-3 regions based on pre-COVID-19 data. Estimates are then used to predict regional recovery growth out of sample and to quantify the growth contribution of RDI funding. Depending on the chosen scenario, our out-of-sample predictions point to a mean recovery growth rate of GDP between ~2-4 % in 2021 after a decline of up to -2.5 % in 2020. RDI funding constitutes a significant pillar of the recovery process with mean contributions in terms of GDP growth of between 0.4 and 1 %-points.
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Affiliation(s)
- Timo Mitze
- University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Teemu Makkonen
- University of Eastern Finland, Yliopistokatu 2, 80101 Joensuu, Finland
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Schmitz T, Lakes T, Manafa G, Lambio C, Butler J, Roth A, Savaskan N. Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting. Front Public Health 2023; 11:1128452. [PMID: 37124802 PMCID: PMC10133460 DOI: 10.3389/fpubh.2023.1128452] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/24/2023] [Indexed: 05/02/2023] Open
Abstract
The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.
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Affiliation(s)
- Tillman Schmitz
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
- *Correspondence: Tillman Schmitz,
| | - Tobia Lakes
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
- Integrative Research Institute on Transformations of Human Environment Systems (IRI THESys), Berlin, Germany
| | - Georgianna Manafa
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Christoph Lambio
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Jeffrey Butler
- Applied Geoinformation Science, Geography Department, Humboldt University Berlin, Berlin, Germany
| | - Alexandra Roth
- Department of Public Health Neukölln, District Office Neukölln, Berlin, Germany
| | - Nicolai Savaskan
- Department of Public Health Neukölln, District Office Neukölln, Berlin, Germany
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28
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N'konzi JPN, Chukwu CW, Nyabadza F. Effect of time-varying adherence to non-pharmaceutical interventions on the occurrence of multiple epidemic waves: A modeling study. Front Public Health 2022; 10:1087683. [PMID: 36605240 PMCID: PMC9807866 DOI: 10.3389/fpubh.2022.1087683] [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: 11/02/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Non-pharmaceutical interventions (NPIs) play a central role in infectious disease outbreak response and control. Their usefulness cannot be overstated, especially during the early phases of a new epidemic when vaccines and effective treatments are not available yet. These interventions can be very effective in curtailing the spread of infectious diseases when adequately implemented and sufficiently adopted by the public. However, NPIs can be very disruptive, and the socioeconomic and cultural hardships that come with their implementation interfere with both the ability and willingness of affected populations to adopt such interventions. This can lead to reduced and unsteady adherence to NPIs, making disease control more challenging to achieve. Deciphering this complex interaction between disease dynamics, NPI stringency, and NPI adoption would play a critical role in informing disease control strategies. In this work, we formulate a general-purpose model that integrates government-imposed control measures and public adherence into a deterministic compartmental epidemic model and study its properties. By combining imitation dynamics and the health belief model to encode the unsteady nature of NPI adherence, we investigate how temporal variations in NPI adherence levels affect the dynamics and control of infectious diseases. Among the results, we note the occurrence of multiple epidemic waves as a result of temporal variations in NPI adherence and a trade-off between the stringency of control measures and adherence. Additionally, our results suggest that interventions that aim at increasing public adherence to NPIs are more beneficial than implementing more stringent measures. Our findings highlight the necessity of taking the socioeconomic and cultural realities of affected populations into account when devising public health interventions.
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Affiliation(s)
- Joel-Pascal Ntwali N'konzi
- African Institute for Mathematical Sciences, Kigali, Rwanda,Maxwell Institute for Mathematical Sciences, University of Edinburgh, Heriot-Watt University, Edinburgh, United Kingdom,*Correspondence: Joel-Pascal Ntwali N'konzi
| | - Chidozie Williams Chukwu
- Department of Mathematics and Applied Mathematics, University of Johannesburg, Johannesburg, South Africa
| | - Farai Nyabadza
- Department of Mathematics and Applied Mathematics, University of Johannesburg, Johannesburg, South Africa
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Germann TC, Smith MZ, Dauelsberg LR, Fairchild G, Turton TL, Gorris ME, Ross CW, Ahrens JP, Hemphill DD, Manore CA, Del Valle SY. Assessing K-12 school reopenings under different COVID-19 Spread scenarios - United States, school year 2020/21: A retrospective modeling study. Epidemics 2022; 41:100632. [PMID: 36182803 PMCID: PMC9490957 DOI: 10.1016/j.epidem.2022.100632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/01/2022] [Accepted: 09/20/2022] [Indexed: 02/08/2023] Open
Abstract
INTRODUCTION School-age children play a key role in the spread of airborne viruses like influenza due to the prolonged and close contacts they have in school settings. As a result, school closures and other non-pharmaceutical interventions were recommended as the first line of defense in response to the novel coronavirus pandemic (COVID-19). METHODS We used an agent-based model that simulates communities across the United States including daycares, primary, and secondary schools to quantify the relative health outcomes of reopening schools for the period of August 15, 2020 to April 11, 2021. Our simulation was carried out in early September 2020 and was based on the latest (at the time) Centers for Disease Control and Prevention (CDC)'s Pandemic Planning Scenarios released in May 2020. We explored different reopening scenarios including virtual learning, in-person school, and several hybrid options that stratify the student population into cohorts in order to reduce exposure and pathogen spread. RESULTS Scenarios where cohorts of students return to school in non-overlapping formats, which we refer to as hybrid scenarios, resulted in significant decreases in the percentage of symptomatic individuals with COVID-19, by as much as 75%. These hybrid scenarios have only slightly more negative health impacts of COVID-19 compared to implementing a 100% virtual learning scenario. Hybrid scenarios can significantly avert the number of COVID-19 cases at the national scale-approximately between 28 M and 60 M depending on the scenario-over the simulated eight-month period. We found the results of our simulations to be highly dependent on the number of workplaces assumed to be open for in-person business, as well as the initial level of COVID-19 incidence within the simulated community. CONCLUSION In an evolving pandemic, while a large proportion of people remain susceptible, reducing the number of students attending school leads to better health outcomes; part-time in-classroom education substantially reduces health risks.
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Affiliation(s)
- Timothy C Germann
- Physics & Chemistry of Materials, Los Alamos National Laboratory, USA
| | - Manhong Z Smith
- Information Systems & Modeling, Los Alamos National Laboratory, USA; Center for Nonlinear Studies, Los Alamos National Laboratory, USA
| | | | | | | | - Morgan E Gorris
- Information Systems & Modeling, Los Alamos National Laboratory, USA; Center for Nonlinear Studies, Los Alamos National Laboratory, USA
| | - Chrysm Watson Ross
- Information Systems & Modeling, Los Alamos National Laboratory, USA; Computer Science Department, University of New Mexico, USA
| | - James P Ahrens
- National Security Education Center, Los Alamos National Laboratory, USA
| | - Daniel D Hemphill
- Advanced Research in Cyber Systems, Los Alamos National Laboratory, USA
| | - Carrie A Manore
- Information Systems & Modeling, Los Alamos National Laboratory, USA
| | - Sara Y Del Valle
- Information Systems & Modeling, Los Alamos National Laboratory, USA.
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30
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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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Kim YR, Choi YJ, Min Y. A model of COVID-19 pandemic with vaccines and mutant viruses. PLoS One 2022; 17:e0275851. [PMID: 36279292 PMCID: PMC9591069 DOI: 10.1371/journal.pone.0275851] [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: 05/18/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
This paper proposes a compartment model (SVEIHRM model) based on a system of ordinary differential equations to simulate the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Emergence of mutant viruses gave rise to multiple peaks in the number of confirmed cases. Vaccine developers and WHO suggest individuals to receive multiple vaccinations (the primary and the secondary vaccinations and booster shots) to mitigate transmission of COVID-19. Taking this into account, we include compartments for multiple vaccinations and mutant viruses of COVID-19 in the model. In particular, our model considers breakthrough infection according to the antibody formation rate following multiple vaccinations. We obtain the effective reproduction numbers of the original virus, the Delta, and the Omicron variants by fitting this model to data in Korea. Additionally, we provide various simulations adjusting the daily vaccination rate and the timing of vaccination to investigate the effects of these two vaccine-related measures on the number of infected individuals. We also show that starting vaccinations early is the key to reduce the number of infected individuals. Delaying the start date requires increasing substantially the rate of vaccination to achieve similar target results. In the sensitivity analysis on the vaccination rate of Korean data, it is shown that a 10% increase (decrease) in vaccination rates can reduce (increase) the number of confirmed cases by 35.22% (82.82%), respectively.
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Affiliation(s)
- Young Rock Kim
- Major in Mathematics Education, Graduate School of Education, Hankuk University of Foreign Studies, Seoul, Republic of Korea
| | - Yong-Jae Choi
- Economics Division, Hankuk University of Foreign Studies, Seoul, Republic of Korea
| | - Youngho Min
- Major in Mathematics Education, Graduate School of Education, Hankuk University of Foreign Studies, Seoul, Republic of Korea
- * E-mail:
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32
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Strasser ZH, Greifer N, Hadavand A, Murphy SN, Estiri H. Estimates of SARS-CoV-2 Omicron BA.2 Subvariant Severity in New England. JAMA Netw Open 2022; 5:e2238354. [PMID: 36282501 PMCID: PMC9597387 DOI: 10.1001/jamanetworkopen.2022.38354] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
IMPORTANCE The SARS-CoV-2 Omicron subvariant, BA.2, may be less severe than previous variants; however, confounding factors make interpreting the intrinsic severity challenging. OBJECTIVE To compare the adjusted risks of mortality, hospitalization, intensive care unit admission, and invasive ventilation between the BA.2 subvariant and the Omicron and Delta variants, after accounting for multiple confounders. DESIGN, SETTING, AND PARTICIPANTS This was a retrospective cohort study that applied an entropy balancing approach. Patients in a multicenter inpatient and outpatient system in New England with COVID-19 between March 3, 2020, and June 20, 2022, were identified. EXPOSURES Cases were assigned as being exposed to the Delta (B.1.617.2) variant, the Omicron (B.1.1.529) variant, or the Omicron BA.2 lineage subvariants. MAIN OUTCOMES AND MEASURES The primary study outcome planned before analysis was risk of 30-day mortality. Secondary outcomes included the risks of hospitalization, invasive ventilation, and intensive care unit admissions. RESULTS Of 102 315 confirmed COVID-19 cases (mean [SD] age, 44.2 [21.6] years; 63 482 women [62.0%]), 20 770 were labeled as Delta variants, 52 605 were labeled as the Omicron B.1.1.529 variant, and 28 940 were labeled as Omicron BA.2 subvariants. Patient cases were excluded if they occurred outside the prespecified temporal windows associated with the variants or had minimal longitudinal data in the Mass General Brigham system before COVID-19. Mortality rates were 0.7% for Delta (B.1.617.2), 0.4% for Omicron (B.1.1.529), and 0.3% for Omicron (BA.2). The adjusted odds ratio of mortality from the Delta variant compared with the Omicron BA.2 subvariants was 2.07 (95% CI, 1.04-4.10) and that of the original Omicron variant compared with the Omicron BA.2 subvariant was 2.20 (95% CI, 1.56-3.11). For all outcomes, the Omicron BA.2 subvariants were significantly less severe than that of the Omicron and Delta variants. CONCLUSIONS AND RELEVANCE In this cohort study, after having accounted for a variety of confounding factors associated with SARS-CoV-2 outcomes, the Omicron BA.2 subvariant was found to be intrinsically less severe than both the Delta and Omicron variants. With respect to these variants, the severity profile of SARS-CoV-2 appears to be diminishing after taking into account various factors including therapeutics, vaccinations, and prior infections.
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Affiliation(s)
- Zachary H. Strasser
- MGH Laboratory of Computer Science, Massachusetts General Hospital, Boston
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Noah Greifer
- Institute for Quantitative Social Science, Harvard University, Cambridge, Massachusetts
| | - Aboozar Hadavand
- College of Computational Science, Minerva University, San Francisco, California
| | - Shawn N. Murphy
- MGH Laboratory of Computer Science, Massachusetts General Hospital, Boston
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Hossein Estiri
- MGH Laboratory of Computer Science, Massachusetts General Hospital, Boston
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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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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Banholzer N, Lison A, Özcelik D, Stadler T, Feuerriegel S, Vach W. The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review. Eur J Epidemiol 2022; 37:1003-1024. [PMID: 36152133 PMCID: PMC9510554 DOI: 10.1007/s10654-022-00908-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.
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Affiliation(s)
- Nicolas Banholzer
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
| | - Dennis Özcelik
- Chemistry | Biology | Pharmacy Information Center, ETH Zurich, Zurich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- LMU Munich School of Management, LMU Munich, Munich, Germany
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland
- Department of Environmental Sciences, University of Basel, Basel, Switzerland
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35
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Mueed A, Ahmad T, Abdullah M, Sultan F, Khan AA. Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000648. [PMID: 36962567 PMCID: PMC10022346 DOI: 10.1371/journal.pgph.0000648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/24/2022] [Indexed: 11/19/2022]
Abstract
Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. A single-group interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools, on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered.
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Affiliation(s)
- Abdul Mueed
- Akhter Hameed Khan Foundation, Islamabad, Pakistan
| | | | | | - Faisal Sultan
- Ministry of National Health Services, Regulation and Coordination, Islamabad, Pakistan
| | - Adnan Ahmad Khan
- Ministry of National Health Services, Regulation and Coordination, Islamabad, Pakistan
- Research and Development Solutions, Islamabad, Pakistan
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36
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Solomon H, Thea DM, Galea S, Sabin LL, Lucey DR, Hamer DH. Adherence to and enforcement of non-pharmaceutical interventions (NPIs) for COVID-19 prevention in Nigeria, Rwanda, and Zambia: A mixed-methods analysis. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000389. [PMID: 36962721 PMCID: PMC10022265 DOI: 10.1371/journal.pgph.0000389] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/01/2022] [Indexed: 11/18/2022]
Abstract
INTRODUCTION In the early parts of the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were implemented worldwide, including in sub-Saharan Africa, to prevent and control SARS-CoV-2 transmission. This mixed-methods study examines adherence to and enforcement of NPIs implemented to curb COVID-19 in Nigeria, Rwanda, and Zambia, leading up to the 10,000th case of laboratory-confirmed COVID-19 in each country. Additionally, we aim to evaluate the relationship between levels and changes of NPIs over time and changes in COVID-19 cases and deaths. METHODS This mixed-methods analysis utilized semi-structured interviews and a quantitative dataset constructed using multiple open data sources, including the Oxford COVID-19 Government Response Tracker. To understand potential barriers and facilitators in implementing and enforcing NPIs qualitative data were collected from those involved in the COVID-19 response and analyzed using NVivo. Quantitative results were analyzed using descriptive statistics, plots, ANOVA, and post hoc Tukey. RESULTS Individual indicator scores varied with the COVID-19 response in all three countries. Nigeria had sustained levels of strict measures for containment and closure NPIs, while in Rwanda there was substantial variation in NPI score as it transitioned through the different case windows for the same measures. Zambia implemented moderate stringency throughout the pandemic using gathering restrictions and business/school closure measures but maintained low levels of strictness for other containment and closure measures. Rwanda had far more consistent and stringent measures compared to Nigeria and Zambia. Rwanda's success in implementing COVID-related measures was partly due to strong enforcement and having a population that generally follow the recommendations of their government. CONCLUSION Various forces either facilitated or hindered adherence and compliance to COVID-19 control measures. The lessons learned and recommendations gleaned through interviews with experts involved in the COVID-19 pandemic and quantitative analysis of NPI implementation can be applied to future outbreaks, epidemics, and pandemics. Recommendations include engaging communities, using a risk-based approach to implement containment and closure NPIs, and providing social and economic support to citizens during periods of lockdowns and other measures that interrupt the ability to make a living.
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Affiliation(s)
- Hiwote Solomon
- Doctor of Public Health Program, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Donald M. Thea
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Sandro Galea
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Lora L. Sabin
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Daniel R. Lucey
- Department of Medicine, Georgetown University Medical Center, Washington, District of Columbia, United States of America
| | - Davidson H. Hamer
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
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Redlin M. Differences in NPI strategies against COVID-19. JOURNAL OF REGULATORY ECONOMICS 2022; 62:1-23. [PMID: 36035787 PMCID: PMC9395806 DOI: 10.1007/s11149-022-09452-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
Non-pharmaceutical interventions are an effective strategy to prevent and control COVID-19 transmission in the community. However, the timing and stringency to which these measures have been implemented varied between countries and regions. The differences in stringency can only to a limited extent be explained by the number of infections and the prevailing vaccination strategies. Our study aims to shed more light on the lockdown strategies and to identify the determinants underlying the differences between countries on regional, economic, institutional, and political level. Based on daily panel data for 173 countries and the period from January 2020 to October 2021 we find significant regional differences in lockdown strategies. Further, more prosperous countries implemented milder restrictions but responded more quickly, while poorer countries introduced more stringent measures but had a longer response time. Finally, democratic regimes and stronger manifested institutions alleviated and slowed down the introduction of lockdown measures.
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Affiliation(s)
- Margarete Redlin
- Department of Economics, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
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38
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Lu B, Zhu L. Public health events emergency management supervision strategy considering citizens’ and new media’s different ways of participation. Soft comput 2022; 26:11749-11769. [PMID: 35992193 PMCID: PMC9378273 DOI: 10.1007/s00500-022-07380-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2022] [Indexed: 01/08/2023]
Abstract
Public health events have done great harm. Emergency management requires the joint participation of multiple parties including government department, pharmaceutical enterprises, citizens and new media. Then, what are the effects of different strategy choices in participation of citizens and new media on emergency management? To answer the question, we construct a four-party evolutionary game model, considering the citizens' two participation ways consisted of true evaluation and false evaluation, and the new media's two participation ways consisted of report after verification and report without verification. This is of more practical significance than simply studying whether citizens and new media participate in emergency management or not because citizen and new media participation does not represent the completely positive behavior. Then, we conduct the evolutionary stability analysis, solve the stable equilibrium points using the Lyapunov first method and conduct the simulation analysis with MATLAB 2020b. The results show that, firstly, the greater the probability of citizens making true evaluation, the more inclined the government department is to strictly implement the emergency management system; secondly, when the probability of citizens making true evaluation decreases, new media are more inclined to report after verification, and when new media lose more pageview value or should be punished more for reporting without verification, the probability that they report without verification is smaller; thirdly, the greater the probability of citizens making false evaluation, the less enthusiasm of pharmaceutical enterprises to participate in emergency management, which indicates that false evaluation is detrimental to prompt pharmaceutical enterprises to participate; what's more, the greater the probability of new media reporting after verification, the greater the probability of pharmaceutical enterprises actively participating, which shows that new media's verification to citizens' evaluation is beneficial to emergency management.
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Affiliation(s)
- Bingjie Lu
- School of Business, Shandong Normal University, Jinan, 250014 China
- Quality Research Center, Shandong Normal University, Jinan, 250014 China
| | - Lilong Zhu
- School of Business, Shandong Normal University, Jinan, 250014 China
- Quality Research Center, Shandong Normal University, Jinan, 250014 China
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Huisman JS, Scire J, Angst DC, Li J, Neher RA, Maathuis MH, Bonhoeffer S, Stadler T. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. eLife 2022; 11:71345. [PMID: 35938911 DOI: 10.1101/2020.11.26.20239368] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/01/2022] [Indexed: 05/28/2023] Open
Abstract
The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.
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Affiliation(s)
- Jana S Huisman
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Jérémie Scire
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Daniel C Angst
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Jinzhou Li
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Richard A Neher
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Biozentrum, University of Basel, Basel, Switzerland
| | - Marloes H Maathuis
- Department of Mathematics, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Tanja Stadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
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Huisman JS, Scire J, Angst DC, Li J, Neher RA, Maathuis MH, Bonhoeffer S, Stadler T. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. eLife 2022; 11:71345. [PMID: 35938911 PMCID: PMC9467515 DOI: 10.7554/elife.71345] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/01/2022] [Indexed: 11/20/2022] Open
Abstract
The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data. Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining. Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re. Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time. For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place. The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.
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Affiliation(s)
- Jana Sanne Huisman
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Daniel C Angst
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Jinzhou Li
- Department of Mathematics, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
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Zhang Y, Hui FKP, Duffield C, Saeed AM. A review of facilities management interventions to mitigate respiratory infections in existing buildings. BUILDING AND ENVIRONMENT 2022; 221:109347. [PMID: 35782231 PMCID: PMC9238148 DOI: 10.1016/j.buildenv.2022.109347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/01/2022] [Accepted: 06/23/2022] [Indexed: 06/15/2023]
Abstract
The Covid-19 pandemic reveals that the hazard of the respiratory virus was a secondary consideration in the design, development, construction, and management of public and commercial buildings. Retrofitting such buildings poses a significant challenge for building owners and facilities managers. This article reviews current research and practices in building operations interventions for indoor respiratory infection control from the perspective of facilities managers to assess the effectiveness of available solutions. This review systematically selects and synthesises eighty-six articles identified through the PRISMA process plus supplementary articles identified as part of the review process, that deal with facilities' operations and maintenance (O&M) interventions. The paper reviewed the context, interventions, mechanisms, and outcomes discussed in these articles, concluding that interventions for respiratory virus transmission in existing buildings fall into three categories under the Facilities Management (FM) discipline: Hard services (HVAC and drainage system controls) to prevent aerosol transmissions, Soft Services (cleaning and disinfection) to prevent fomite transmissions, and space management (space planning and occupancy controls) to eliminate droplet transmissions. Additionally, the research emphasised the need for FM intervention studies that examine occupant behaviours with integrated intervention results and guide FM intervention decision-making. This review expands the knowledge of FM for infection control and highlights future research opportunities.
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Affiliation(s)
- Yan Zhang
- Department of Infrastructure Engineering, University of Melbourne, Level 6, Building 290, 700 Swanston Street, Carlton, Victoria, Australia
| | - Felix Kin Peng Hui
- Department of Infrastructure Engineering, University of Melbourne, Australia
| | - Colin Duffield
- Department of Infrastructure Engineering, University of Melbourne, Australia
| | - Ali Mohammed Saeed
- Department of Jobs, Regions and Precincts, Level 13, 1 Spring Street, Melbourne, Victoria, Australia
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Investigating the Effectiveness of Government Public Health Systems against COVID-19 by Hybrid MCDM Approaches. MATHEMATICS 2022. [DOI: 10.3390/math10152678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To elucidate the effectiveness of the containment strategies against the pandemic, a Multi-Criteria Decision Making (MCDM) model is established to evaluate the government’s performance against COVID-19. In this study, the Analytic Hierarchy Process (AHP), Entropy, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used in determining the performance of the public health system. We adopt both subjective and objective weighting methods for a more accurate evaluation. In addition, the evaluation of performance against COVID-19 is conducted in various aspects and divided into different periods. Data Envelopment Analysis (DEA) is applied to evaluate the sustainability of the public health system. Composite scores of the public health system are determined based on the performance and sustainability assessment. The five countries, South Korea, Japan, Germany, Australia, and China are rated with higher composite scores. On the country, the US, Indonesia, Egypt, South Africa, and Brazil receive lower rating scores among the countries for evaluation. This modeling study can provide a practical quantitative justification for developing containment policies and suggestions for improving the public health system in more countries or areas.
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Salvatore M, Purkayastha S, Ganapathi L, Bhattacharyya R, Kundu R, Zimmermann L, Ray D, Hazra A, Kleinsasser M, Solomon S, Subbaraman R, Mukherjee B. Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience. SCIENCE ADVANCES 2022; 8:eabp8621. [PMID: 35714183 PMCID: PMC9205583 DOI: 10.1126/sciadv.abp8621] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.
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Affiliation(s)
- Maxwell Salvatore
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Lakshmi Ganapathi
- Division of Infectious Diseases, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Ritoban Kundu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Lauren Zimmermann
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Aditi Hazra
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Sunil Solomon
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ramnath Subbaraman
- Department of Public Health and Community Medicine and Center for Global Public Health, Tufts University School of Medicine, Boston, MA, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Corresponding author.
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Dimarco G, Toscani G, Zanella M. Optimal control of epidemic spreading in the presence of social heterogeneity. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210160. [PMID: 35400193 DOI: 10.1098/rsta.2021.0160] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/03/2021] [Indexed: 06/14/2023]
Abstract
The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal, carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyse the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits us to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectiveness of the proposed selective measures to dampen the epidemic trends. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.
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Affiliation(s)
- G Dimarco
- Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
| | - G Toscani
- Department of Mathematics 'F. Casorati', University of Pavia, Pavia, Italy
- Institute for Applied Mathematics and Information Technologies (IMATI), Via Ferrata, 5/A, Pavia, Italy
| | - M Zanella
- Department of Mathematics 'F. Casorati', University of Pavia, Pavia, Italy
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Banholzer N, Feuerriegel S, Vach W. Estimating and explaining cross-country variation in the effectiveness of non-pharmaceutical interventions during COVID-19. Sci Rep 2022; 12:7526. [PMID: 35534516 PMCID: PMC9085796 DOI: 10.1038/s41598-022-11362-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/22/2022] [Indexed: 12/15/2022] Open
Abstract
To control the COVID-19 pandemic, countries around the world have implemented non-pharmaceutical interventions (NPIs), such as school closures or stay-at-home orders. Previous work has estimated the effectiveness of NPIs, yet without examining variation in NPI effectiveness across countries. Based on data from the first epidemic wave of \documentclass[12pt]{minimal}
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\begin{document}$$n=40$$\end{document}n=40 countries, we estimate country-specific differences in the effectiveness of NPIs via a semi-mechanistic Bayesian hierarchical model. Our estimates reveal substantial variation between countries, indicating that NPIs have been more effective in some countries (e. g. Switzerland, New Zealand, and Iceland) as compared to others (e. g. Singapore, South Africa, and France). We then explain differences in the effectiveness of NPIs through 12 country characteristics (e. g. population age, urbanization, employment, etc.). A positive association with country-specific effectiveness of NPIs was found for government effectiveness, gross domestic product (GDP) per capita, population ages 65+, and health expenditures. Conversely, a negative association with effectiveness of NPIs was found for the share of informal employment, average household size and population density. Overall, the wealth and demographic structure of a country can explain variation in the effectiveness of NPIs.
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Nasri K, Boubaker H, Dhaouadi N. Dynamic governance of the first wave of Covid-19 in Tunisia: An interoperability analysis. WORLD MEDICAL & HEALTH POLICY 2022; 14:366-381. [PMID: 35601471 PMCID: PMC9111154 DOI: 10.1002/wmh3.508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 12/19/2021] [Accepted: 01/18/2022] [Indexed: 11/16/2022]
Abstract
This study proposes an interoperability index of the measures taken by the Tunisian government during the first wave of the coronavirus disease 2019 (COVID‐19) pandemic. In the first part, we present the process of decision making as a revised and adjusted process in continuous upgrading, based on the dynamic governance process in times of crisis. In the second part, we estimate an index that records the strictness of government policies in each subperiod and the degree of interoperability between the Tunisian pandemic responses against COVID‐19 using subperiod instantiations. Our empirical findings show that the pandemic management strategy in Tunisia during the first wave was adjusted by incorporating new pandemic policies and changing the stringency levels over time. After estimating the interoperability index, we found that the measures taken early in a subperiod interact directly with the next successive subperiod in the decision process, but they interact indirectly with other successive subperiods. The pandemic management strategy in Tunisia during the first wave has been adjusted by incorporating new pandemic policies and changing the stringency levels over time. Tunisia has reached the highest level of the strictness of government policies, after 18 days of initial responses taken during the first wave in a stepwise manner. The measures taken early in a subperiod interact directly with the next successive subperiod in the decision‐making process, but they interact indirectly with other successive subperiods. Pandemic crisis cannot be managed or defeated with a single measure or policy, even at the highest stringency level. Instead, it is managed with several policy responses that interreact together over time. The establishment of a dynamic and flexible decision‐making process can be useful in managing a future health crisis in countries whose public health systems suffer from several shortcomings.
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Affiliation(s)
- Khaled Nasri
- FESGT University of Tunis EL Manar Tunis Tunisia
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47
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Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries. SUSTAINABILITY 2022. [DOI: 10.3390/su14063694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Based on data from 121 countries, the study assesses the dynamic effect and causality path of the government epidemic prevention policies and human mobility behaviors on the growth rates of COVID-19 new cases and deaths. Our results find that both policies and behaviors influenced COVID-19 cases and deaths. The direct effect of policies on COVID-19 was more than the indirect effect. Policies influence behaviors, and behaviors react spontaneously to information. Further, masks give people a false sense of security and increase mobility. The close public transport policy increased COVID-19 new cases. We also conducted sensitivity analysis and found that some policies hold robustly, such as the policies of school closing, restrictions on gatherings, stay-at-home requirements, international travel controls, facial coverings, and vaccination. The counterfactual tests suggest that, as of early March 2021, if governments had mandated masking policies early in the epidemic, the cases and deaths would have been reduced by 18% and 14% separately. If governments had implemented vaccination policies early in the pandemic, the cases and deaths would have been reduced by 93% and 62%, respectively. Without public transportation closures, cases and deaths would have been reduced by 40% and 10%, respectively.
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Lison A, Persson J, Banholzer N, Feuerriegel S. Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020. EURO SURVEILLANCE : BULLETIN EUROPEEN SUR LES MALADIES TRANSMISSIBLES = EUROPEAN COMMUNICABLE DISEASE BULLETIN 2022; 27. [PMID: 35272745 PMCID: PMC8915405 DOI: 10.2807/1560-7917.es.2022.27.10.2100374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction Human mobility was considerably reduced during the COVID-19 pandemic. To support disease surveillance, it is important to understand the effect of mobility on transmission. Aim We compared the role of mobility during the first and second COVID-19 wave in Switzerland by studying the link between daily travel distances and the effective reproduction number (Rt) of SARS-CoV-2. Methods We used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first (2 March–7 April 2020) and second wave (1 October–10 December 2020). Results Daily travel distances decreased by 73% in the first and by 44% in the second wave (relative to February 2020). For a 1% reduction in average daily travel distance, Rt was estimated to decline by 0.73% (95% credible interval (CrI): 0.34–1.03) in the first wave and by 1.04% (95% CrI: 0.66–1.42) in the second wave. The estimated mobility effects were similar in both waves for all modes of transport, travel purposes and sociodemographic subgroups but differed for movement radius. Conclusion Mobility was associated with SARS-CoV-2 Rt during the first two epidemic waves in Switzerland. The relative effect of mobility was similar in both waves, but smaller mobility reductions in the second wave corresponded to smaller overall reductions in Rt. Mobility data from mobile phones have a continued potential to support real-time surveillance of COVID-19.
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Spiegel M, Tookes H. All or nothing? Partial business shutdowns and COVID-19 fatality growth. PLoS One 2022; 17:e0262925. [PMID: 35139100 PMCID: PMC8827474 DOI: 10.1371/journal.pone.0262925] [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: 07/28/2021] [Accepted: 01/05/2022] [Indexed: 11/19/2022] Open
Abstract
Incomplete vaccine uptake and limited vaccine availability for some segments of the population could lead policymakers to consider re-imposing restrictions to help reduce fatalities. Early in the pandemic, full business shutdowns were commonplace. Given this response, much of the literature on policy effectiveness has focused on full closures and their impact. But were complete closures necessary? Using a hand-collected database of partial business closures for all U.S. counties from March through December 2020, we examine the impact of capacity restrictions on COVID-19 fatality growth. For the restaurant and bar sector, we find that several combinations of partial capacity restrictions are as effective as full shutdowns. For example, point estimates indicate that, for the average county, limiting restaurants and bars to 25% of capacity reduces the fatality growth rate six weeks ahead by approximately 43%, while completely closing them reduces fatality growth by about 16%. The evidence is more mixed for the other sectors that we study. We find that full gym closures reduce the COVID-19 fatality growth rate, while partial closures may be counterproductive relative to leaving capacity unrestricted. Retail closures are ineffective, but 50% capacity limits reduce fatality growth. We find that restricting salons, other personal services and movie theaters is either ineffective or counterproductive.
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Affiliation(s)
- Matthew Spiegel
- Yale School of Management, Yale University, New Haven, Connecticut, United States of America
| | - Heather Tookes
- Yale School of Management, Yale University, New Haven, Connecticut, United States of America
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50
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Cohen DA, Talarowski M, Awomolo O, Han B, Williamson S, McKenzie TL. Increased mask adherence after important politician infected with COVID-19. PLoS One 2022; 17:e0261398. [PMID: 35020749 PMCID: PMC8754325 DOI: 10.1371/journal.pone.0261398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 12/01/2021] [Indexed: 11/19/2022] Open
Abstract
Objectives
To quantify changes in adherence to mask and distancing guidelines in outdoor settings in Philadelphia, PA before and after President Trump announced he was infected with COVID-19.
Methods
We used Systematic Observation of Masking Adherence and Distancing (SOMAD) to assess mask adherence in parks, playgrounds, and commercial streets in the 10 City Council districts in Philadelphia PA. We compared adherence rates between August and September 2020 and after October 2, 2020.
Results
Disparities in mask adherence existed by age group, gender, and race/ethnicity, with females wearing masks correctly more often than males, seniors having higher mask use than other age groups, and Asians having higher adherence than other race/ethnicities. Correct mask use did not increase after the City released additional mask guidance in September but did after Oct 2. Incorrect mask use also decreased, but the percentage not having masks at all was unchanged.
Conclusions
Vulnerability of leadership appears to influence population behavior. Public health departments likely need more resources to effectively and persuasively communicate critical safety messages related to COVID-19 transmission.
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Affiliation(s)
- Deborah A. Cohen
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA, United States of America
- * E-mail:
| | | | | | - Bing Han
- RAND Corporation, Santa Monica, CA, United States of America
| | | | - Thomas L. McKenzie
- Emeritus, San Diego State University, San Diego, CA, United States of America
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