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Loster R, Smook S, Humphrey L, Lyver D, Mohammadi Z, Thommes EW, Cojocaru MG. Behaviour quantification of public health policy adoption - the case of non-pharmaceutical measures during COVID-19. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2025; 22:920-942. [PMID: 40296797 DOI: 10.3934/mbe.2025033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2025]
Abstract
In this work, we provide estimates of non-pharmaceutical interventions (NPIs) adoption and its effects on the COVID-19 disease transmission across the province of Ontario, Canada, in 2020. Using freely available data, we estimate perceived risks of infection and a personal discomfort with complying with NPIs for Ontarians across 34 public health units. With the use of game theory, we model a time series of decision making processes in each public health region to extract an estimate of the adoption level of NPIs from March to December 2020. In conjunction with a susceptible-exposed-recovered-isolated compartmental model for Ontario, we are able to estimate a province-wide effectiveness level of NPIs. Last but not least, we show the model's versatility by applying it to Pennsylvania and Georgia in the United States.
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Affiliation(s)
- Rhiannon Loster
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Sarah Smook
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Lia Humphrey
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - David Lyver
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Zahra Mohammadi
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Edward W Thommes
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
- Sanofi, 1755 Steeles Ave W, North York, ON M2R 3T4, Canada
| | - Monica G Cojocaru
- Department of Mathematics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
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2
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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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3
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Bali Y, Bajiya VP, Tripathi JP, Mubayi A. Exploring data sources and mathematical approaches for estimating human mobility rates and implications for understanding COVID-19 dynamics: a systematic literature review. J Math Biol 2024; 88:67. [PMID: 38641762 DOI: 10.1007/s00285-024-02082-z] [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: 08/31/2022] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
Human mobility, which refers to the movement of people from one location to another, is believed to be one of the key factors shaping the dynamics of the COVID-19 pandemic. There are multiple reasons that can change human mobility patterns, such as fear of an infection, control measures restricting movement, economic opportunities, political instability, etc. Human mobility rates are complex to estimate as they can occur on various time scales, depending on the context and factors driving the movement. For example, short-term movements are influenced by the daily work schedule, whereas long-term trends can be due to seasonal employment opportunities. The goal of the study is to perform literature review to: (i) identify relevant data sources that can be used to estimate human mobility rates at different time scales, (ii) understand the utilization of variety of data to measure human movement trends under different contexts of mobility changes, and (iii) unraveling the associations between human mobility rates and social determinants of health affecting COVID-19 disease dynamics. The systematic review of literature was carried out to collect relevant articles on human mobility. Our study highlights the use of three major sources of mobility data: public transit, mobile phones, and social surveys. The results also provides analysis of the data to estimate mobility metrics from the diverse data sources. All major factors which directly and indirectly influenced human mobility during the COVID-19 spread are explored. Our study recommends that (a) a significant balance between primitive and new estimated mobility parameters need to be maintained, (b) the accuracy and applicability of mobility data sources should be improved, (c) encouraging broader interdisciplinary collaboration in movement-based research is crucial for advancing the study of COVID-19 dynamics among scholars from various disciplines.
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Affiliation(s)
- Yogesh Bali
- Department of Mathematics, Central University of Rajasthan, Kishangarh, Ajmer, 305817, India
| | - Vijay Pal Bajiya
- Department of Mathematics, Central University of Rajasthan, Kishangarh, Ajmer, 305817, India
| | - Jai Prakash Tripathi
- Department of Mathematics, Central University of Rajasthan, Kishangarh, Ajmer, 305817, India.
| | - Anuj Mubayi
- Intercollegiate Biomathematics Alliance, Illinois State University, Normal, USA
- Kalam Institute of Health Technology, Visakhapatnam, India
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4
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Lyver D, Nica M, Cot C, Cacciapaglia G, Mohammadi Z, Thommes EW, Cojocaru MG. Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5604-5633. [PMID: 38872550 DOI: 10.3934/mbe.2024247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so-called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use USA county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.
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Affiliation(s)
- David Lyver
- Department of Mathematics, University of Guelph, Guelph ON N1G 2W1, Canada
| | - Mihai Nica
- Department of Mathematics, University of Guelph, Guelph ON N1G 2W1, Canada
| | - Corentin Cot
- Laboratoire de Physique des 2 Infinis Irène Joliot Curie (UMR 9012), CNRS/IN2P3, Orsay 91400, France
| | - Giacomo Cacciapaglia
- Institut de Physique des 2 Infinis de Lyon (UMR 5822), CNRS/IN2P3 et Université Claude Bernard Lyon 1, Villeurbanne 69622, France
| | - Zahra Mohammadi
- Department of Mathematics, University of Guelph, Guelph ON N1G 2W1, Canada
| | - Edward W Thommes
- Department of Mathematics, University of Guelph, Guelph ON N1G 2W1, Canada
- Sanofi, North York ON M2R 3T4, Canada
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5
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Zhao Y, Wong SWK. A comparative study of compartmental models for COVID-19 transmission in Ontario, Canada. Sci Rep 2023; 13:15050. [PMID: 37700081 PMCID: PMC10497623 DOI: 10.1038/s41598-023-42043-y] [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: 10/31/2022] [Accepted: 09/04/2023] [Indexed: 09/14/2023] Open
Abstract
The number of confirmed COVID-19 cases reached over 1.3 million in Ontario, Canada by June 4, 2022. The continued spread of the virus underlying COVID-19 has been spurred by the emergence of variants since the initial outbreak in December, 2019. Much attention has thus been devoted to tracking and modelling the transmission of COVID-19. Compartmental models are commonly used to mimic epidemic transmission mechanisms and are easy to understand. Their performance in real-world settings, however, needs to be more thoroughly assessed. In this comparative study, we examine five compartmental models-four existing ones and an extended model that we propose-and analyze their ability to describe COVID-19 transmission in Ontario from January 2022 to June 2022.
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Affiliation(s)
- Yuxuan Zhao
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada
| | - Samuel W K Wong
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada.
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6
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Abdulrashid I, Friji H, Topuz K, Ghazzai H, Delen D, Massoud Y. An analytical approach to evaluate the impact of age demographics in a pandemic. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:1-15. [PMID: 37362847 PMCID: PMC10248992 DOI: 10.1007/s00477-023-02477-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/13/2023] [Indexed: 06/28/2023]
Abstract
The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.
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Affiliation(s)
- Ismail Abdulrashid
- School of Finance and Operations Management, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104 USA
| | - Hamdi Friji
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07030 USA
| | - Kazim Topuz
- School of Finance and Operations Management, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104 USA
| | - Hakim Ghazzai
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia
| | - Dursun Delen
- Department of Management Science and Information Systems, Oklahoma State University, Tulsa, OK 74106 USA
- Faculty of Engineering and Natural Sciences, Department of Industrial Engineering, Istinye University, Istanbul, Turkey
| | - Yehia Massoud
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia
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Abuauf M, Raboei EH, Alshareef M, Rabie N, Al-Zailai R, Alharbi A, Felemban W, Al Nasser I, Shalabi H. Corona virus 19(COVID-19) Conceptual Modeling a Single-Center Prospective: Cross-Sectional Study. JMIR Form Res 2023. [PMID: 37256829 DOI: 10.2196/41376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Conceptual models are abstract representations of the real world. They are used to refine medical and non-medical healthcare scopes of service. During the covid 19 pandemic numerous analytic predictive models were generated aiming to evaluate the impact of policies implemented on the mitigating of COVID-19 pandemic, the psycho-social factors that might govern general population adherence to these policies, identify factors that might affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available, and predicted readiness to resume non-COVID-19 healthcare services. OBJECTIVE The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital 1st of March to the 31st of May 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of SARS-CoV-2, minimize mortality, ICU admission, and ensure the safety of healthcare providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission, and the impact of the policy implemented on SARS-CoV-2 positivity among healthcare providers. The secondary outcome of interest was to evaluate the sensitivity of the SARS-coV-2 visual score implemented by the Saudi MOH for COVID-19- risk assessment as well as CURB-65 scores in predicting ICU admission or mortality among the study population. METHODS This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making. RESULTS 275 were SARS-CoV-2- positive within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: the mean age was 42 ± 19.2 years, 19% of the study population were senior adults ≥ 60 years, 80% had a CURB-65 score < 4, 53% had no comorbidities, 5% had extreme obesity, and 2% had a significant hematological abnormality. The overall rate of ICU admission for the study population was 5%, with a 1.5% overall mortality. The multivariate correlation analysis revealed that high selectivity was adopted, wherein patients with complex medical problems were not sent to MOH isolation facilities. Furthermore, 5% of healthcare providers were SARS-CoV-2-positive, and none were healthcare providers allocated to the COVID-19 screening areas indicating the effectiveness of the policy implemented to ensure the safety of healthcare providers. CONCLUSIONS Based on the conceptual model outcome, the selectivity applied to retaining high-risk populations within the hospital might have contributed to the low mortality rate observed without increasing the risk to attending healthcare providers. CLINICALTRIAL Not applicable.
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Affiliation(s)
- Mawahib Abuauf
- Department of pediatric, neonatology king Fahad armed forces hospital Jeddah, Al-DUHA street (65) MISHRIFA 7, Jeddaha, SA
| | - Enaam Hassan Raboei
- king Fahad armed forces hospital Jeddah, Chairperson of the research committee, Head of pediatric Surgery Division. Consultant Pediatric Surgeon, Jeddah, SA
| | - Muneera Alshareef
- king Fahad armed forces hospital Jeddah, Consultant Endocrinologist, Member of hospital research committee, Jeddah, SA
| | - Nada Rabie
- king Fahad armed forces hospital Jeddah, Consultant Infection Disease Adults, Member of hospital research committee, Jeddah, SA
| | - Roaa Al-Zailai
- king Fahad armed forces hospital Jeddah, Consultant Pediatric Infection Disease, Jeddah, SA
| | - Abdullah Alharbi
- king Fahad armed forces hospital Jeddah, Consultant Pathologist, Jeddah, SA
| | - Walaa Felemban
- king Fahad armed forces hospital Jeddah, Consultant Pathology, Jeddah, SA
| | - Ibrahim Al Nasser
- king Fahad armed forces hospital Jeddah, Hospital director, Consultant Radiologist, Jeddah, SA
| | - Hanin Shalabi
- king Fahad armed forces hospital Jeddah, Research and Data Management Specialist, Jeddah, SA
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8
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Mohammadi Z, Cojocaru MG, Thommes EW. Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world. BMC Public Health 2022; 22:1594. [PMID: 35996132 PMCID: PMC9394048 DOI: 10.1186/s12889-022-13921-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic. METHODS In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region's SARS-COV-2 transmission dynamic. RESULTS We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI's, over and above the ones identified in i) and ii). CONCLUSION In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI's) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV.
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Affiliation(s)
- Zahra Mohammadi
- Department of Mathematics & Statistics, University of Guelph, 50 Stone Road E., Guelph, N1G 2W1 Canada
| | - Monica Gabriela Cojocaru
- Department of Mathematics & Statistics, University of Guelph, 50 Stone Road E., Guelph, N1G 2W1 Canada
| | - Edward Wolfgang Thommes
- Department of Mathematics & Statistics, University of Guelph, 50 Stone Road E., Guelph, N1G 2W1 Canada
- Modeling, Epidemiology and Data Science, Sanofi Pasteur, Toronto, Canada
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9
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Liu Y, Liao C, Zhuo L, Tao H. Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10154. [PMID: 36011787 PMCID: PMC9407938 DOI: 10.3390/ijerph191610154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function β(t) to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in β(t) and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.
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Affiliation(s)
- Yuan Liu
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
| | - Chuyao Liao
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
| | - Li Zhuo
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
| | - Haiyan Tao
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
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10
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Wang X, Han Q, Kong JD. Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study. Infect Dis Model 2022; 7:250-260. [PMID: 35665302 PMCID: PMC9142179 DOI: 10.1016/j.idm.2022.05.006] [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: 02/03/2022] [Revised: 05/22/2022] [Accepted: 05/22/2022] [Indexed: 11/02/2022] Open
Abstract
COVID-19 has been prevalent worldwide for about 2 years now and has brought unprecedented challenges to our society. Before vaccines were available, the main disease intervention strategies were non-pharmaceutical. Starting December 2020, in Ontario, Canada, vaccines were approved for administering to vulnerable individuals and gradually expanded to all individuals above the age of 12. As the vaccine coverage reached a satisfactory level among the eligible population, normal social activities resumed and schools reopened starting September 2021. However, when schools reopen for in-person learning, children under the age of 12 are unvaccinated and are at higher risks of contracting the virus. We propose an age-stratified model based on the age and vaccine eligibility of the individuals. We fit our model to the data in Ontario, Canada and obtain a good fitting result. The results show that a relaxed between-group contact rate may trigger future epidemic waves more easily than an increased within-group contact rate. An increasing mixed contact rate of the older group quickly amplifies the daily incidence numbers for both groups whereas an increasing mixed contact rate of the younger group mainly leads to future waves in the younger group alone. The results indicate the importance of accelerating vaccine rollout for younger individuals in mitigating disease spread.
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