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Martignoni MM, Arino J, Hurford A. Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240186. [PMID: 39100176 PMCID: PMC11295893 DOI: 10.1098/rsos.240186] [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: 01/31/2023] [Accepted: 05/01/2024] [Indexed: 08/06/2024]
Abstract
Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.
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Affiliation(s)
- Maria M. Martignoni
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Department of Ecology, Evolution and Behavior, A. Silberman Institute of Life Sciences, Faculty of Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Amy Hurford
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Biology Department and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
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Cardoso FJ, Victor DR, Silva JRD, Guimarães AC, Leal CA, Taveira MR, Alves JG. Physical fitness level and the risk of severe COVID-19: A systematic review. SPORTS MEDICINE AND HEALTH SCIENCE 2023; 5:174-180. [PMID: 37753428 PMCID: PMC10518790 DOI: 10.1016/j.smhs.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 07/11/2023] [Accepted: 07/26/2023] [Indexed: 09/28/2023] Open
Abstract
To verify systematically the association between the status of physical fitness and the risk of severe Coronavirus disease 2019 (COVID-19). This systematic review is in accordance with the Preferred Reporting Items for Systematic Review and Meta Analyses (PRISMA) statement and the eligibility criteria followed the Population, Intervention, Comparison, Outcomes and Study (PICOS) recommendation. PubMed, Embase, SciELO and Cochrane electronic databases were searched. All studies that explored the relationship between the pattern of physical fitness and COVID-19 adverse outcomes (hospitalization, intensive care unit admission, intubation, or mortality), were selected. The quality of the studies was assessed by the specific scale of the Newcastle-Ottawa Scale. A total of seven observational studies were identified in this systematic review; 13 468 patients were included in one case-control study, two cohort studies, and four cross-sectional studies. All studies reported an inverse association between high physical fitness and severe COVID-19 (hospitalization, intensive care admission, or mortality). Only some studies reported comorbidities, especially obesity and cardiovascular disorders, but the results remained unchanged after controlling for comorbidities. The quality of the seven studies included was moderate according to the Newcastle-Ottawa Quality Assessment Scale. The methodological heterogeneity of the studies included did not allow a meta-analysis of the findings. In conclusion, higher physical fitness levels were associated with lower risk of hospitalization, intensive care admissions, and mortality rates among patients with COVID-19.
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Affiliation(s)
- Fortunato José Cardoso
- Departament of Hepatology, Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | | | - José Roberto da Silva
- Departament of Hepatology, Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | | | - Carla Adriane Leal
- Departament of Hepatology, Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | | | - João Guilherme Alves
- Departament of Hepatology, Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
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Zhang Y, Zhang J, Koura YH, Feng C, Su Y, Song W, Kong L. Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries and Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:931. [PMID: 36673700 PMCID: PMC9858854 DOI: 10.3390/ijerph20020931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/28/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.
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Affiliation(s)
- Yaming Zhang
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Jiaqi Zhang
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Yaya Hamadou Koura
- School of Foreign Languages, Yanshan University, Qinhuangdao 066004, China
| | - Changyuan Feng
- Business School, University of Granada, Campus Universitario de Cartuja, 18071 Granada, Spain
| | - Yanyuan Su
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Wenjie Song
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Linghao Kong
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
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Design and Analysis of Hospital Throughput Maximization Algorithm under COVID-19 Pandemic. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8127055. [PMID: 35991132 PMCID: PMC9388262 DOI: 10.1155/2022/8127055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022]
Abstract
Under the global pandemic of COVID-19, public health facilities, such as hospitals, are required to readjust, design, and plan a safe movement flow of people to meet the social distance rules and quarantine COVID-19 and the non-COVID-19 patients to prevent cross-infection. However, readjustments to separate patients have significantly reduced the maximum throughput of public health facilities, worsening already scarce public health resources. Therefore, this paper proposes throughput maximization algorithms based on the one-way street problem which meets the requirements of social distance rules. First, the floor plan of a hospital is transformed into a graph, each node is traversed by breadth-first search. Then, this paper considers patients' node pair sets as different set unions, the direction of edges, and the color of links based on DFS-XOR algorithm are designed to distinguish the paths of COVID-19 and non-COVID-19 patients. Finally, this paper utilizes minimum shared link algorithms to determine the minimized sharing links between paths linking different set unions and components. The throughput is maximized by reducing the number of shared links and alternating links. The results indicate that compared with the brute force algorithms, the algorithms proposed in this paper significantly improve the maximum throughput.
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Li M, Ma S, Liu Z. A novel method to detect the early warning signal of COVID-19 transmission. BMC Infect Dis 2022; 22:626. [PMID: 35850664 PMCID: PMC9289935 DOI: 10.1186/s12879-022-07603-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/07/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It's necessary to forecast early warning signals of potential outbreaks of COVID-19, which would facilitate the health ministry to take some suitable control measures timely to prevent or slow the spread of COVID-19. However, since the intricacy of COVID-19 transmission, which connects biological and social systems, it is a difficult task to predict outbreaks of COVID-19 epidemics timely. RESULTS In this work, we developed a new model-free approach, called, the landscape network entropy based on Auto-Reservoir Neural Network (ARNN-LNE), for quantitative analysis of COVID-19 propagation, by mining dynamic information from regional networks and short-term high-dimensional time-series data. Through this approach, we successfully identified the early warning signals in six nations or areas based on historical data of COVID-19 infections. CONCLUSION Based on the newly published data on new COVID-19 disease, the ARNN-LNE method can give early warning signals for the outbreak of COVID-19. It's worth noting that ARNN-LNE only relies on small samples data. Thus, it has great application potential for monitoring outbreaks of infectious diseases.
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Affiliation(s)
- Mingzhang Li
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China
| | - Shuo Ma
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China
| | - Zhengrong Liu
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
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Rando HM, Brueffer C, Lordan R, Dattoli AA, Manheim D, Meyer JG, Mundo AI, Perrin D, Mai D, Wellhausen N, Gitter A, Greene CS. Molecular and Serologic Diagnostic Technologies for SARS-CoV-2. ARXIV 2022:arXiv:2204.12598v2. [PMID: 35547240 PMCID: PMC9094103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 04/28/2022] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.
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Affiliation(s)
- Halie M Rando
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552); the National Human Genome Research Institute (R01 HG010067)
| | | | - Ronan Lordan
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-5158, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Anna Ada Dattoli
- Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David Manheim
- 1DaySooner, Delaware, United States of America; Risk and Health Communication Research Center, School of Public Health, University of Haifa, Haifa, Israel; Technion, Israel Institute of Technology, Haifa, Israel · Funded by Center for Effective Altruism, Long Term Future Fund
| | - Jesse G Meyer
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America · Funded by National Institute of General Medical Sciences (R35 GM142502)
| | - Ariel I Mundo
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
| | - Dimitri Perrin
- School of Computer Science, Queensland University of Technology, Brisbane, Australia; Centre for Data Science, Queensland University of Technology, Brisbane, Australia
| | - David Mai
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Center for Cellular Immunotherapies, Perelman School of Medicine, and Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA, USA
| | - Nils Wellhausen
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Morgridge Institute for Research, Madison, Wisconsin, United States of America · Funded by John W. and Jeanne M. Rowe Center for Research in Virology
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, Pennsylvania, United States of America; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552); the National Human Genome Research Institute (R01 HG010067)
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