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Nunes MC, Thommes E, Fröhlich H, Flahault A, Arino J, Baguelin M, Biggerstaff M, Bizel-Bizellot G, Borchering R, Cacciapaglia G, Cauchemez S, Barbier--Chebbah A, Claussen C, Choirat C, Cojocaru M, Commaille-Chapus C, Hon C, Kong J, Lambert N, Lauer KB, Lehr T, Mahe C, Marechal V, Mebarki A, Moghadas S, Niehus R, Opatowski L, Parino F, Pruvost G, Schuppert A, Thiébaut R, Thomas-Bachli A, Viboud C, Wu J, Crépey P, Coudeville L. Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infect Dis Model 2024; 9:501-518. [PMID: 38445252 PMCID: PMC10912817 DOI: 10.1016/j.idm.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.
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Affiliation(s)
- Marta C. Nunes
- Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon (HCL) and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé Publique, Épidémiologie et Écologie Évolutive des Maladies Infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard Lyon 1, Lyon, France
- South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Edward Thommes
- New Products and Innovation (NPI), Sanofi Vaccines (Global), Toronto, Ontario, Canada
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT (b-it), Bonn, Germany
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland and Swiss School of Public Health, Zürich, Switzerland
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew Biggerstaff
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Gaston Bizel-Bizellot
- Departement of Computational Biology, Departement of Global Health, Institut Pasteur, Paris, France
| | - Rebecca Borchering
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Giacomo Cacciapaglia
- Institut de Physique des Deux Infinis de Lyon (IP2I), UMR5822, IN2P3/CNRS, Université Claude Bernard Lyon 1, Villeurbanne, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000 CNRS, Paris, France
| | - Alex Barbier--Chebbah
- Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR 3571, France
| | - Carsten Claussen
- Fraunhofer-Institute for Translational Medicine and Pharmacology, Hamburg, Germany
| | - Christine Choirat
- Institute of Global Health, Faculty of Medicine, University of Geneva, Switzerland
| | - Monica Cojocaru
- Mathematics & Statistics Department, College of Engineering and Physical Sciences, University of Guelph, Guelph, Ontario, Canada
| | | | - Chitin Hon
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
| | - Jude Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | | | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | | | - Vincent Marechal
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France
| | | | - Seyed Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Lulla Opatowski
- UMR 1018, Team “Anti-infective Evasion and Pharmacoepidemiology”, Université Paris-Saclay, UVSQ, INSERM, France
- Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, Paris, France
| | - Francesco Parino
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | | | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Rodolphe Thiébaut
- Bordeaux University, Department of Public Health, Inserm UMR 1219 Bordeaux Population Health Research Center, Inria SISTM, Bordeaux, France
| | | | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Jianhong Wu
- York Emergency Mitigation, Engagement, Response, and Governance Institute, Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
| | - Pascal Crépey
- EHESP, Université de Rennes, CNRS, IEP Rennes, Arènes - UMR 6051, RSMS – Inserm U 1309, Rennes, France
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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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Milwid RM, Gabriele-Rivet V, Ogden NH, Turgeon P, Fazil A, London D, de Montigny S, Rees EE. A methodology for estimating SARS-CoV-2 importation risk by air travel into Canada between July and November 2021. BMC Public Health 2024; 24:1088. [PMID: 38641571 PMCID: PMC11027292 DOI: 10.1186/s12889-024-18563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 04/09/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Estimating rates of disease importation by travellers is a key activity to assess both the risk to a country from an infectious disease emerging elsewhere in the world and the effectiveness of border measures. We describe a model used to estimate the number of travellers infected with SARS-CoV-2 into Canadian airports in 2021, and assess the impact of pre-departure testing requirements on importation risk. METHODS A mathematical model estimated the number of essential and non-essential air travellers infected with SARS-CoV-2, with the latter requiring a negative pre-departure test result. The number of travellers arriving infected (i.e. imported cases) depended on air travel volumes, SARS-CoV-2 exposure risk in the departure country, prior infection or vaccine acquired immunity, and, for non-essential travellers, screening from pre-departure molecular testing. Importation risk was estimated weekly from July to November 2021 as the number of imported cases and percent positivity (PP; i.e. imported cases normalised by travel volume). The impact of pre-departure testing was assessed by comparing three scenarios: baseline (pre-departure testing of all non-essential travellers; most probable importation risk given the pre-departure testing requirements), counterfactual scenario 1 (no pre-departure testing of fully vaccinated non-essential travellers), and counterfactual scenario 2 (no pre-departure testing of non-essential travellers). RESULTS In the baseline scenario, weekly imported cases and PP varied over time, ranging from 145 to 539 cases and 0.15 to 0.28%, respectively. Most cases arrived from the USA, Mexico, the United Kingdom, and France. While modelling suggested that essential travellers had a higher weekly PP (0.37 - 0.65%) than non-essential travellers (0.12 - 0.24%), they contributed fewer weekly cases (62 - 154) than non-essential travellers (84 - 398 per week) given their lower travel volume. Pre-departure testing was estimated to reduce imported cases by one third (counterfactual scenario 1) to one half (counterfactual scenario 2). CONCLUSIONS The model results highlighted the weekly variation in importation by traveller group (e.g., reason for travel and country of departure) and enabled a framework for measuring the impact of pre-departure testing requirements. Quantifying the contributors of importation risk through mathematical simulation can support the design of appropriate public health policy on border measures.
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Affiliation(s)
- Rachael M Milwid
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Vanessa Gabriele-Rivet
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada.
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada.
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Patricia Turgeon
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Guelph, ON, Canada
| | - David London
- Physique Des Particules, Université de Montréal, Faculté Des Arts Et Des Sciences, Montréal, QC, Canada
| | - Simon de Montigny
- Emergency Management Branch, Global Public Health Intelligence Network Tiger Team, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
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Grunnill M, Arino J, McCarthy Z, Bragazzi NL, Coudeville L, Thommes EW, Amiche A, Ghasemi A, Bourouiba L, Tofighi M, Asgary A, Baky-Haskuee M, Wu J. Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup. PLoS Comput Biol 2024; 20:e1011018. [PMID: 38236838 PMCID: PMC10796029 DOI: 10.1371/journal.pcbi.1011018] [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: 03/12/2023] [Accepted: 11/20/2023] [Indexed: 01/22/2024] Open
Abstract
The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.
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Affiliation(s)
- Martin Grunnill
- Laboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Zachary McCarthy
- Laboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada
| | - Nicola Luigi Bragazzi
- Laboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada
| | | | - Edward W. Thommes
- Modeling, Epidemiology and Data Science (MEDS), Sanofi, Lyon, France
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | | | - Abbas Ghasemi
- The Fluid Dynamics of Disease Transmission Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Mechanical and Industrial Engineering Department, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Lydia Bourouiba
- The Fluid Dynamics of Disease Transmission Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Mohammadali Tofighi
- Dahdaleh Institute for Global Health Research, York University, Toronto, Canada
- Disaster & Emergency Management, York University, Toronto, Canada
| | - Ali Asgary
- Disaster & Emergency Management, York University, Toronto, Canada
- York Emergency Mitigation, Response, Engagement and Governance Institute, York University, Toronto, Ontario, Canada
| | | | - Jianhong Wu
- Laboratory of Industrial and Applied Mathematics (LIAM), York University, Toronto, Ontario, Canada
- York Emergency Mitigation, Response, Engagement and Governance Institute, York University, Toronto, Ontario, Canada
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5
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He R, Luo X, Asamoah JKK, Zhang Y, Li Y, Jin Z, Sun GQ. A hierarchical intervention scheme based on epidemic severity in a community network. J Math Biol 2023; 87:29. [PMID: 37452969 DOI: 10.1007/s00285-023-01964-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 06/01/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023]
Abstract
As there are no targeted medicines or vaccines for newly emerging infectious diseases, isolation among communities (villages, cities, or countries) is one of the most effective intervention measures. As such, the number of intercommunity edges ([Formula: see text]) becomes one of the most important factor in isolating a place since it is closely related to normal life. Unfortunately, how [Formula: see text] affects epidemic spread is still poorly understood. In this paper, we quantitatively analyzed the impact of [Formula: see text] on infectious disease transmission by establishing a four-dimensional [Formula: see text] edge-based compartmental model with two communities. The basic reproduction number [Formula: see text] is explicitly obtained subject to [Formula: see text] [Formula: see text]. Furthermore, according to [Formula: see text] with zero [Formula: see text], epidemics spread could be classified into two cases. When [Formula: see text] for the case 2, epidemics occur with at least one of the reproduction numbers within communities greater than one, and otherwise when [Formula: see text] for case 1, both reproduction numbers within communities are less than one. Remarkably, in case 1, whether epidemics break out strongly depends on intercommunity edges. Then, the outbreak threshold in regard to [Formula: see text] is also explicitly obtained, below which epidemics vanish, and otherwise break out. The above two cases form a severity-based hierarchical intervention scheme for epidemics. It is then applied to the SARS outbreak in Singapore, verifying the validity of our scheme. In addition, the final size of the system is gained by demonstrating the existence of positive equilibrium in a four-dimensional coupled system. Theoretical results are also validated through numerical simulation in networks with the Poisson and Power law distributions, respectively. Our results provide a new insight into controlling epidemics.
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Affiliation(s)
- Runzi He
- Department of Mathematics, North University of China, Shanxi, Taiyuan, 030051, China
| | - Xiaofeng Luo
- Department of Mathematics, North University of China, Shanxi, Taiyuan, 030051, China.
| | - Joshua Kiddy K Asamoah
- Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Yongxin Zhang
- Department of Mathematics, North University of China, Shanxi, Taiyuan, 030051, China
| | - Yihong Li
- Department of Mathematics, North University of China, Shanxi, Taiyuan, 030051, China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Shanxi, Taiyuan, 030006, China
| | - Gui-Quan Sun
- Department of Mathematics, North University of China, Shanxi, Taiyuan, 030051, China.
- Complex Systems Research Center, Shanxi University, Shanxi, Taiyuan, 030006, China.
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Csörgő M, Dawson DA, Nasri BR, Rémillard BN. A random walk through Canadian contributions on empirical processes and their applications in probability and statistics. CAN J STAT 2022. [DOI: 10.1002/cjs.11730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Miklós Csörgő
- School of Mathematics and Statistics Carleton University 1125 Colonel By Drive Ottawa Ontario Canada K1S 5B6
| | - Donald A. Dawson
- School of Mathematics and Statistics Carleton University 1125 Colonel By Drive Ottawa Ontario Canada K1S 5B6
| | - Bouchra R. Nasri
- Département de médecine sociale et préventive, École de santé publique Université de Montréal C.P. 6128, succursale Centre‐ville Montréal Montreal Québec Canada H3C 3J7
| | - Bruno N. Rémillard
- Department of Decision Sciences, HEC Montréal 3000 chemin de la Côte Sainte‐Catherine Montréal Québec Canada H3T 2A7
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Arino J, Milliken E. Bistability in deterministic and stochastic SLIAR-type models with imperfect and waning vaccine protection. J Math Biol 2022; 84:61. [PMID: 35737177 PMCID: PMC9219406 DOI: 10.1007/s00285-022-01765-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 04/20/2022] [Accepted: 05/27/2022] [Indexed: 11/30/2022]
Abstract
Various vaccines have been approved for use to combat COVID-19 that offer imperfect immunity and could furthermore wane over time. We analyze the effect of vaccination in an SLIARS model with demography by adding a compartment for vaccinated individuals and considering disease-induced death, imperfect and waning vaccination protection as well as waning infections-acquired immunity. When analyzed as systems of ordinary differential equations, the model is proven to admit a backward bifurcation. A continuous time Markov chain (CTMC) version of the model is simulated numerically and compared to the results of branching process approximations. While the CTMC model detects the presence of the backward bifurcation, the branching process approximation does not. The special case of an SVIRS model is shown to have the same properties.
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Affiliation(s)
- Julien Arino
- Department of Mathematics & Data Science Nexus, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Evan Milliken
- Department of Mathematics, University of Louisville, Louisville, Kentucky, United States.
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8
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Zhang Z, Liu C, Nunkoo R, Sunnassee VA, Chen X. Rethinking Lockdown Policies in the Pre-Vaccine Era of COVID-19: A Configurational Perspective. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:7142. [PMID: 35742409 PMCID: PMC9223109 DOI: 10.3390/ijerph19127142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/04/2022] [Accepted: 06/08/2022] [Indexed: 12/10/2022]
Abstract
The significance of lockdown policies for controlling the COVID-19 pandemic is widely recognized. However, most studies have focused on individual lockdown measures. The effectiveness of lockdown policy combinations has not been examined from a configurational perspective. This research applies fuzzy-set qualitative comparative analysis (fsQCA) to examine different lockdown policy combinations associated with high-epidemic situations in 84 countries. A high-epidemic situation can occur through three different "weak-confined" patterns of lockdown policy combinations. The findings demonstrate that a combination of lockdown policies is more successful than any single lockdown policy, whereas the absence of several key measures in policy combinations can lead to a high-epidemic situation. The importance of international travel controls can become obscured when they are the only measures adopted, and a high-epidemic situation can still arise where restrictions are placed on international travel but not on public transport or when workplaces are closed but schools remain open.
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Affiliation(s)
- Ziang Zhang
- School of Geography, Nanjing Normal University, Nanjing 210023, China;
| | - Chao Liu
- Faculty of Hospitality & Tourism Management, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Robin Nunkoo
- Department of Management, University of Mauritius, Reduit MU 80837, Mauritius;
- School of Tourism and Hospitality, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa
- Griffith Institute for Tourism, Griffith University, Gold Coast, QLD 4222, Australia
- Copenhagen Business School, Porcelaenshaven 18A, DK-2000 Frederiksberg, Denmark
| | - Vivek A. Sunnassee
- Westminster Business School, University of Westminster, 35 Marylebone Road, London NW1 5LS, UK;
| | - Xiaoyan Chen
- School of Humanities, Jiangsu University of Technology, Changzhou 213001, China;
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9
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Ledda C, Costantino C, Motta G, Cunsolo R, Stracquadanio P, Liberti G, Maltezou HC, Rapisarda V. SARS-CoV-2 mRNA Vaccine Breakthrough Infections in Fully Vaccinated Healthcare Personnel: A Systematic Review. Trop Med Infect Dis 2022; 7:9. [PMID: 35051125 PMCID: PMC8781002 DOI: 10.3390/tropicalmed7010009] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 12/20/2022] Open
Abstract
The number of people vaccinated against COVID-19 increases worldwide every day; however, it is important to study the risk of breakthrough infections in vaccinated individuals at high risk of exposure such as healthcare personnel (HCP). A systematic literature review (SLR) applying the PRISMA declaration and the PECOS format using the following entry terms was used: "Health Personnel OR Healthcare Worker OR Healthcare Provider OR Healthcare Personnel AND breakthrough OR infection after vaccine*". The research was carried out utilizing the following databases: SCOPUS, PubMed, Embase, and Web of Sciences. An overall very low incidence of post-vaccination breakthrough infections was found, ranging from 0.011 to 0.001 (per 100 individuals at risk). Our findings further support the published high effectiveness rates of mRNA vaccines in preventing SARS-CoV-2 infections among fully vaccinated HCP. Additional studies are needed to define the duration of the vaccine-induced protection among HCP.
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Affiliation(s)
- Caterina Ledda
- Occupational Medicine Unit, Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy;
| | - Claudio Costantino
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, 90133 Palermo, Italy;
| | - Giuseppe Motta
- Occupational Medicine Unit, “Garibaldi” Hospital of Catania, 95123 Catania, Italy;
| | - Rosario Cunsolo
- Hospital Health Management, “G. Rodolico-San Marco” Polyclinic University Hospital, 95123 Catania, Italy;
| | - Patrizia Stracquadanio
- Occupational Medicine Unit, “G. Rodolico-San Marco” University Hospital, 95123 Catania, Italy;
| | - Giuseppe Liberti
- Commissioner Office in Acta for the COVID-19 Emergency, Provincial Health Authority of Catania, 95123 Catania, Italy;
| | - Helena C. Maltezou
- Directorate for Research, Studies and Documentation, National Public Health Organization, 15123 Athens, Greece;
| | - Venerando Rapisarda
- Occupational Medicine Unit, Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy;
- Occupational Medicine Unit, “G. Rodolico-San Marco” University Hospital, 95123 Catania, Italy;
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