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Hrzic R, Cade MV, Wong BLH, McCreesh N, Simon J, Czabanowska K. A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates. J Public Health (Oxf) 2024; 46:127-135. [PMID: 38061776 PMCID: PMC10901273 DOI: 10.1093/pubmed/fdad248] [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: 07/10/2023] [Revised: 10/04/2023] [Accepted: 11/09/2023] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education. METHODS The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation. RESULTS The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation. CONCLUSIONS The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.
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Affiliation(s)
- Rok Hrzic
- Department of International Health, Care and Public Health Research Institute - CAPHRI, Maastricht University, Maastricht, 6200 MD, Netherlands
| | - Maria Vitoria Cade
- Department of International Health, Care and Public Health Research Institute - CAPHRI, Maastricht University, Maastricht, 6200 MD, Netherlands
| | - Brian Li Han Wong
- Department of International Health, Care and Public Health Research Institute - CAPHRI, Maastricht University, Maastricht, 6200 MD, Netherlands
| | - Nicky McCreesh
- Department of Infectious Disease Epidemiology and Dynamics, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Judit Simon
- Department of Health Economics, Center for Public Health, Medical University of Vienna, Vienna, 1090, Austria
| | - Katarzyna Czabanowska
- Department of International Health, Care and Public Health Research Institute - CAPHRI, Maastricht University, Maastricht, 6200 MD, Netherlands
- Department of Health Policy Management, Institute of Public Health, Jagiellonian University, Krakow, 31-066, Poland
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Yang L, Iwami M, Chen Y, Wu M, van Dam KH. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. PROGRESS IN PLANNING 2023; 168:100657. [PMID: 35280114 PMCID: PMC8904142 DOI: 10.1016/j.progress.2022.100657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.
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Affiliation(s)
- Liu Yang
- School of Architecture, Southeast University, Nanjing, China
- Research Center of Urban Design, Southeast University, Nanjing, China
| | - Michiyo Iwami
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, UK
| | - Yishan Chen
- Architecture and Urban Design Research Center, China IPPR International Engineering CO., LTD, Beijing, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Koen H van Dam
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
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Paul R, Han D, DeDoncker E, Prieto D. Dynamic downscaling and daily nowcasting from influenza surveillance data. Stat Med 2022; 41:4159-4175. [PMID: 35718471 PMCID: PMC9544787 DOI: 10.1002/sim.9502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 04/30/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022]
Abstract
Real-time trends from surveillance data are important to assess and develop preparedness for influenza outbreaks. The overwhelming testing demand and limited capacity of testing laboratories for viral positivity render daily confirmed case data inaccurate and delay its availability in preparedness. Using Bayesian dynamic downscaling models, we obtained posterior estimates for daily influenza incidences from weekly estimates of the Centers for Disease Control and Prevention and daily reported constitutional and respiratory complaints during emergency department (ED) visits obtained from the state health departments. Our model provides one-day and seven-day lead forecasts along with 95 % $$ \% $$ prediction intervals. Our hybrid Markov Chain Monte Carlo and Kalman filter algorithms facilitate faster computation and enable us to update our estimates as new data become available. Our method is tested and validated using the State of Michigan data over the years 2009-2013. Reported constitutional and respiratory complaints at the EDs showed strong correlations of 0.81 and 0.68 respectively, with influenza rates. In general, our forecast model can be adapted to track an outbreak with only one respiratory virus as a causative agent.
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Affiliation(s)
- Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina, USA
| | - Dan Han
- Department of Mathematics, University of Louisville, Louisville, Kentucky, USA
| | - Elise DeDoncker
- Department of Computer Science, Western Michigan University, Kalamazoo, Michigan, USA
| | - Diana Prieto
- Carey School of Business, Johns Hopkins University, Baltimore, Maryland, USA.,School of Industrial Engineering, Pontificia Universdad de Catòlica de Valparaìso, Valparaìso, Chile
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Olesen SW, Trabert E. Infectious Disease Modeling: Recommendations for Public Health Decision-Makers. Disaster Med Public Health Prep 2022; 16:1-3. [PMID: 35652654 DOI: 10.1017/dmp.2022.99] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Infectious disease modeling plays an important role in the response to infectious disease outbreaks, perhaps most notably during the coronavirus disease 2019 (COVID-19) pandemic. In our experience working with state and local governments during COVID-19 and previous public health crises, we have observed that, while the scientific literature focuses on models' accuracy and underlying assumptions, an important limitation on the effective application of modeling to public health decision-making is the ability of decision-makers and modelers to work together productively. We therefore propose a set of guiding principles, informed by our experience, for working relationships between decision-makers and modelers. We hypothesize that these guidelines will improve the utility of infectious disease modeling for public health decision-making, irrespective of the particular outbreak in question and of the precise modeling approaches being used.
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Affiliation(s)
- Scott W Olesen
- Center for Public Health Preparedness and Resilience, Institute for Public Research, CNA, Arlington, Virginia, USA
| | - Eric Trabert
- Center for Public Health Preparedness and Resilience, Institute for Public Research, CNA, Arlington, Virginia, USA
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5
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Rosenbaum JE, Stillo M, Graves N, Rivera R. Timeliness of provisional United States mortality data releases during the COVID-19 pandemic: delays associated with electronic death registration system and weekly mortality. J Public Health Policy 2021; 42:536-549. [PMID: 34732841 PMCID: PMC8564267 DOI: 10.1057/s41271-021-00309-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2021] [Indexed: 01/19/2023]
Abstract
All-cause mortality counts allow public health authorities to identify populations experiencing excess deaths from pandemics, natural disasters, and other emergencies. Delays in the completeness of mortality counts may contribute to misinformation because death counts take weeks to become accurate. We estimate the timeliness of all-cause mortality releases during the COVID-19 pandemic for the dates 3 April to 5 September 2020 by estimating the number of weekly data releases of the NCHS Fluview Mortality Surveillance System until mortality comes within 99% of the counts in the 19 March 19 2021 provisional mortality data release. States' mortality counts take 5 weeks at median (interquartile range 4-7 weeks) to completion. The fastest states were Maine, New Hampshire, Vermont, New York, Utah, Idaho, and Hawaii. States that had not adopted the electronic death registration system (EDRS) were 4.8 weeks slower to achieve complete mortality counts, and each weekly death per 10^8 was associated with a 0.8 week delay. Emergency planning should improve the timeliness of mortality data by improving state vital statistics digital infrastructure.
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Affiliation(s)
- Janet E Rosenbaum
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, 450 Clarkson Ave, MS 43, Brooklyn, NY, 11203, USA.
| | - Marco Stillo
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, 450 Clarkson Ave, MS 43, Brooklyn, NY, 11203, USA
| | - Nathaniel Graves
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, 450 Clarkson Ave, MS 43, Brooklyn, NY, 11203, USA
| | - Roberto Rivera
- College of Business, University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico
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6
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Shankar S, Mohakuda SS, Kumar A, Nazneen P, Yadav AK, Chatterjee K, Chatterjee K. Systematic review of predictive mathematical models of COVID-19 epidemic. Med J Armed Forces India 2021; 77:S385-S392. [PMID: 34334908 PMCID: PMC8313025 DOI: 10.1016/j.mjafi.2021.05.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/04/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models. METHODS Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail. RESULTS Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions. CONCLUSION There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.
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Affiliation(s)
- Subramanian Shankar
- Consultant (Medicine & Clinical Immunology), Air Cmde AFMS (P&T), O/o DGAFMS, New Delhi, India
| | | | - Ankit Kumar
- Resident, Department of Internal Medicine, Armed Forces Medical College, Pune, India
| | - P.S. Nazneen
- Resident, Department of Internal Medicine, Armed Forces Medical College, Pune, India
| | - Arun Kumar Yadav
- Associate Professor, Department of Community Medicine, Armed Forces Medical College, Pune, India
| | - Kaushik Chatterjee
- Professor & Head, Department of Psychiatry, Armed Forces Medical College, Pune, India
| | - Kaustuv Chatterjee
- Officer-in-Charge, School of Medical Assistants, INHS Asvini, Mumbai, India
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Rivera R, Rosenbaum JE, Quispe W. Excess mortality in the United States during the first three months of the COVID-19 pandemic. Epidemiol Infect 2020; 148:e264. [PMID: 33115546 PMCID: PMC7653492 DOI: 10.1017/s0950268820002617] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/21/2020] [Accepted: 10/27/2020] [Indexed: 12/15/2022] Open
Abstract
Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013-127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338-6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095-3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646-6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341-3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562-7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170-16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538-39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125-6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.
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Affiliation(s)
- R. Rivera
- College of Business, University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico
| | - J. E. Rosenbaum
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - W. Quispe
- College of Business, University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico
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8
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Shi P, Yan J, Keskinocak P, Shane AL, Swann JL. The impact of opening dedicated clinics on disease transmission during an influenza pandemic. PLoS One 2020; 15:e0236455. [PMID: 32760086 PMCID: PMC7410326 DOI: 10.1371/journal.pone.0236455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 07/05/2020] [Indexed: 11/19/2022] Open
Abstract
Dedicated clinics can be established in an influenza pandemic to isolate people and potentially reduce opportunities for influenza transmission. However, their operation requires resources and their existence may attract the worried-well. In this study, we quantify the impact of opening dedicated influenza clinics during a pandemic based on an agent-based simulation model across a time-varying social network of households, workplaces, schools, community locations, and health facilities in the state of Georgia. We calculate performance measures, including peak prevalence and total attack rate, while accounting for clinic operations, including timing and location. We find that opening clinics can reduce disease spread and hospitalizations even when visited by the worried-well, open for limited weeks, or open in limited locations, and especially when the clinics are in operation during times of highest prevalence. Specifically, peak prevalence, total attack rate, and hospitalization reduced 0.07–0.32%, 0.40–1.51%, 0.02–0.09%, respectively, by operating clinics for the pandemic duration.
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Affiliation(s)
- Pengyi Shi
- Krannert School of Management, Purdue University, West Lafayette, Indiana, United States of America
| | - Jia Yan
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Pinar Keskinocak
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Andi L. Shane
- Division of Infectious Diseases, Department of Pediatrics, Emory University and Children’s Healthcare of Atlanta, Atlanta, Georgia, United States of America
| | - Julie L. Swann
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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9
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Drolet M, Bénard É, Jit M, Hutubessy R, Brisson M. Model Comparisons of the Effectiveness and Cost-Effectiveness of Vaccination: A Systematic Review of the Literature. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2018; 21:1250-1258. [PMID: 30314627 DOI: 10.1016/j.jval.2018.03.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 03/20/2018] [Accepted: 03/25/2018] [Indexed: 05/21/2023]
Abstract
OBJECTIVES To describe all published articles that have conducted comparisons of model-based effectiveness and cost-effectiveness results in the field of vaccination. Specific objectives were to 1) describe the methodologies used and 2) identify the strengths and limitations of the studies. METHODS We systematically searched MEDLINE and Embase databases for studies that compared predictions of effectiveness and cost-effectiveness of vaccination of two or more mathematical models. We categorized studies into two groups on the basis of their data source for comparison (previously published results or new simulation results) and performed a qualitative synthesis of study conclusions. RESULTS We identified 115 eligible articles (only 5% generated new simulations from the reviewed models) examining the effectiveness and cost-effectiveness of vaccination against 14 pathogens (69% of studies examined human papillomavirus, influenza, and/or pneumococcal vaccines). The goal of most of studies was to summarize evidence for vaccination policy decisions, and cost-effectiveness was the most frequent outcome examined. Only 33%, 25%, and 3% of studies followed a systematic approach to identify eligible studies, assessed the quality of studies, and performed a quantitative synthesis of results, respectively. A greater proportion of model comparisons using published studies followed a systematic approach to identify eligible studies and to assess their quality, whereas more studies using new simulations performed quantitative synthesis of results and identified drivers of model conclusions. Most comparative modeling studies concluded that vaccination was cost-effective. CONCLUSIONS Given the variability in methods used to conduct/report comparative modeling studies, guidelines are required to enhance their quality and transparency and to provide better tools for decision making.
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Affiliation(s)
- Mélanie Drolet
- Centre de recherche du CHU de Québec-Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, Canada
| | - Élodie Bénard
- Centre de recherche du CHU de Québec-Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, Canada
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK; Modelling and Economics Unit, Public Health England, London, UK
| | | | - Marc Brisson
- Centre de recherche du CHU de Québec-Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, Canada; Université Laval, Québec, Canada; Department of Infectious Disease Epidemiology, Imperial College, London, UK.
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10
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Ahmed F, Zviedrite N, Uzicanin A. Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review. BMC Public Health 2018. [PMID: 29669545 DOI: 10.1186/s12889-018-5446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Social distancing is one of the community mitigation measures that may be recommended during influenza pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as schools or workplaces. We conducted a systematic review to assess the evidence that social distancing in non-healthcare workplaces reduces or slows influenza transmission. METHODS Electronic searches were conducted using MEDLINE, Embase, Scopus, Cochrane Library, PsycINFO, CINAHL, NIOSHTIC-2, and EconLit to identify studies published in English from January 1, 2000, through May 3, 2017. Data extraction was done by two reviewers independently. A narrative synthesis was performed. RESULTS Fifteen studies, representing 12 modeling and three epidemiological, met the eligibility criteria. The epidemiological studies showed that social distancing was associated with a reduction in influenza-like illness and seroconversion to 2009 influenza A (H1N1). However, the overall risk of bias in the epidemiological studies was serious. The modeling studies estimated that workplace social distancing measures alone produced a median reduction of 23% in the cumulative influenza attack rate in the general population. It also delayed and reduced the peak influenza attack rate. The reduction in the cumulative attack rate was more pronounced when workplace social distancing was combined with other nonpharmaceutical or pharmaceutical interventions. However, the effectiveness was estimated to decline with higher basic reproduction number values, delayed triggering of workplace social distancing, or lower compliance. CONCLUSIONS Modeling studies support social distancing in non-healthcare workplaces, but there is a paucity of well-designed epidemiological studies. SYSTEMATIC REVIEW REGISTRATION NUMBER PROSPERO registration # CRD42017065310.
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Affiliation(s)
- Faruque Ahmed
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.
| | - Nicole Zviedrite
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Amra Uzicanin
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
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11
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Ahmed F, Zviedrite N, Uzicanin A. Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review. BMC Public Health 2018; 18:518. [PMID: 29669545 PMCID: PMC5907354 DOI: 10.1186/s12889-018-5446-1] [Citation(s) in RCA: 221] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/11/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Social distancing is one of the community mitigation measures that may be recommended during influenza pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as schools or workplaces. We conducted a systematic review to assess the evidence that social distancing in non-healthcare workplaces reduces or slows influenza transmission. METHODS Electronic searches were conducted using MEDLINE, Embase, Scopus, Cochrane Library, PsycINFO, CINAHL, NIOSHTIC-2, and EconLit to identify studies published in English from January 1, 2000, through May 3, 2017. Data extraction was done by two reviewers independently. A narrative synthesis was performed. RESULTS Fifteen studies, representing 12 modeling and three epidemiological, met the eligibility criteria. The epidemiological studies showed that social distancing was associated with a reduction in influenza-like illness and seroconversion to 2009 influenza A (H1N1). However, the overall risk of bias in the epidemiological studies was serious. The modeling studies estimated that workplace social distancing measures alone produced a median reduction of 23% in the cumulative influenza attack rate in the general population. It also delayed and reduced the peak influenza attack rate. The reduction in the cumulative attack rate was more pronounced when workplace social distancing was combined with other nonpharmaceutical or pharmaceutical interventions. However, the effectiveness was estimated to decline with higher basic reproduction number values, delayed triggering of workplace social distancing, or lower compliance. CONCLUSIONS Modeling studies support social distancing in non-healthcare workplaces, but there is a paucity of well-designed epidemiological studies. SYSTEMATIC REVIEW REGISTRATION NUMBER PROSPERO registration # CRD42017065310.
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Affiliation(s)
- Faruque Ahmed
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.
| | - Nicole Zviedrite
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Amra Uzicanin
- Community Interventions for Infection Control Unit, Division of Global Migration and Quarantine, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
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12
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Silva W, Das TK, Izurieta R. Estimating disease burden of a potential A(H7N9) pandemic influenza outbreak in the United States. BMC Public Health 2017; 17:898. [PMID: 29178863 PMCID: PMC5702185 DOI: 10.1186/s12889-017-4884-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 10/26/2017] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Since spring 2013, periodic emergence of avian influenza A(H7N9) virus in China has heightened the concern for a possible pandemic outbreak among humans, though it is believed that the virus is not yet human-to-human transmittable. Till June 2017, A(H7N9) has resulted in 1533 laboratory-confirmed cases of human infections causing 592 deaths. The aim of this paper is to present disease burden estimates (measured by infection attack rates (IAR) and number of deaths) in the event of a possible pandemic outbreak caused by human-to-human transmission capability acquired by A(H7N9) virus. Even though such a pandemic will likely spread worldwide, our focus in this paper is to estimate the impact on the United States alone. METHOD The method first uses a data clustering technique to divide 50 states in the U.S. into a small number of clusters. Thereafter, for a few selected states in each cluster, the method employs an agent-based (AB) model to simulate human A(H7N9) influenza pandemic outbreaks. The model uses demographic and epidemiological data. A few selected non-pharmaceutical intervention (NPI) measures are applied to mitigate the outbreaks. Disease burden for the U.S. is estimated by combining results from the clusters applying a method used in stratified sampling. RESULTS Two possible pandemic scenarios with R 0 = 1.5 and 1.8 are examined. Infection attack rates with 95% C.I. (Confidence Interval) for R 0 = 1.5 and 1.8 are estimated to be 18.78% (17.3-20.27) and 25.05% (23.11-26.99), respectively. The corresponding number of deaths (95% C.I.), per 100,000, are 7252.3 (6598.45-7907.33) and 9670.99 (8953.66-10,389.95). CONCLUSIONS The results reflect a possible worst-case scenario where the outbreak extends over all states of the U.S. and antivirals and vaccines are not administered. Our disease burden estimations are also likely to be somewhat high due to the fact that only dense urban regions covering approximately 3% of the geographic area and 81% of the population are used for simulating sample outbreaks. Outcomes from these simulations are extrapolated over the remaining 19% of the population spread sparsely over 97% of the area. Furthermore, the full extent of possible NPIs, if deployed, could also have lowered the disease burden estimates.
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Affiliation(s)
- Walter Silva
- Department of Industrial and Management System Engineering, University of South Florida, Tampa, FL 33620 USA
| | - Tapas K. Das
- Department of Industrial and Management System Engineering, University of South Florida, Tampa, FL 33620 USA
| | - Ricardo Izurieta
- College of Public Health, University of South Florida, Tampa, FL 33620 USA
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Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms. Online J Public Health Inform 2017; 9:e187. [PMID: 29026453 DOI: 10.5210/ojphi.v9i2.8004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.
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Pandemic Risk Assessment Model (PRAM): a mathematical modeling approach to pandemic influenza planning. Epidemiol Infect 2016; 144:3400-3411. [PMID: 27545901 DOI: 10.1017/s0950268816001850] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The Pandemic Risk Assessment Model (PRAM) is a mathematical model developed to analyse two pandemic influenza control measures available to public health: antiviral treatment and immunization. PRAM is parameterized using surveillance data from Alberta, Canada during pandemic H1N1. Age structure and risk level are incorporated in the compartmental, deterministic model through a contact matrix. The model characterizes pandemic influenza scenarios by transmissibility and severity properties. Simulating a worst-case scenario similar to the 1918 pandemic with immediate stockpile release, antiviral demand is 20·3% of the population. With concurrent, effective and timely immunization strategies, antiviral demand would be significantly less. PRAM will be useful in informing policy decisions such as the size of the Alberta antiviral stockpile and can contribute to other pandemic influenza planning activities and scenario analyses.
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Pautasso M, Jeger MJ. Network epidemiology and plant trade networks. AOB PLANTS 2014; 6:plu007. [PMID: 24790128 PMCID: PMC4038442 DOI: 10.1093/aobpla/plu007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 02/11/2014] [Indexed: 05/29/2023]
Abstract
Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.
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Affiliation(s)
- Marco Pautasso
- Forest Pathology and Dendrology, Institute of Integrative Biology, ETHZ, Zurich, Switzerland
| | - Mike J. Jeger
- Division of Ecology and Evolution & Centre for Environmental Policy, Imperial College London, London, UK
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Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One 2014; 9:e94130. [PMID: 24714027 PMCID: PMC3979760 DOI: 10.1371/journal.pone.0094130] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 03/12/2014] [Indexed: 11/18/2022] Open
Abstract
Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.
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Affiliation(s)
- Jean-Paul Chretien
- Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, Silver Spring, Maryland, United States of America
- * E-mail:
| | - Dylan George
- Division of Analytic Decision Support, Biomedical Advanced Research and Development Authority, Department of Health and Human Services, Washington, DC, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Rohit A. Chitale
- Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, Silver Spring, Maryland, United States of America
| | - F. Ellis McKenzie
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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Prieto D, Das TK. An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks. Health Care Manag Sci 2014; 19:1-19. [PMID: 24710651 DOI: 10.1007/s10729-014-9273-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2013] [Accepted: 02/12/2014] [Indexed: 12/20/2022]
Abstract
Uncertainty of pandemic influenza viruses continue to cause major preparedness challenges for public health policymakers. Decisions to mitigate influenza outbreaks often involve tradeoff between the social costs of interventions (e.g., school closure) and the cost of uncontrolled spread of the virus. To achieve a balance, policymakers must assess the impact of mitigation strategies once an outbreak begins and the virus characteristics are known. Agent-based (AB) simulation is a useful tool for building highly granular disease spread models incorporating the epidemiological features of the virus as well as the demographic and social behavioral attributes of tens of millions of affected people. Such disease spread models provide excellent basis on which various mitigation strategies can be tested, before they are adopted and implemented by the policymakers. However, to serve as a testbed for the mitigation strategies, the AB simulation models must be operational. A critical requirement for operational AB models is that they are amenable for quick and simple calibration. The calibration process works as follows: the AB model accepts information available from the field and uses those to update its parameters such that some of its outputs in turn replicate the field data. In this paper, we present our epidemiological model based calibration methodology that has a low computational complexity and is easy to interpret. Our model accepts a field estimate of the basic reproduction number, and then uses it to update (calibrate) the infection probabilities in a way that its effect combined with the effects of the given virus epidemiology, demographics, and social behavior results in an infection pattern yielding a similar value of the basic reproduction number. We evaluate the accuracy of the calibration methodology by applying it for an AB simulation model mimicking a regional outbreak in the US. The calibrated model is shown to yield infection patterns closely replicating the input estimates of the basic reproduction number. The calibration method is also tested to replicate an initial infection incidence trend for a H1N1 outbreak like that of 2009.
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Affiliation(s)
- D Prieto
- Department of Industrial and Manufacturing Engineering, Western Michigan University, 1903 W. Michigan Ave., Kalamazoo, MI, 49008-5336, USA.
| | - T K Das
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, 33620, USA
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Corley CD, Pullum LL, Hartley DM, Benedum C, Noonan C, Rabinowitz PM, Lancaster MJ. Disease prediction models and operational readiness. PLoS One 2014; 9:e91989. [PMID: 24647562 PMCID: PMC3960139 DOI: 10.1371/journal.pone.0091989] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 02/19/2014] [Indexed: 11/18/2022] Open
Abstract
The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.
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Affiliation(s)
- Courtney D. Corley
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
- * E-mail:
| | - Laura L. Pullum
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - David M. Hartley
- Georgetown University Medical Center, Washington, DC, United States of America
| | - Corey Benedum
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
| | - Christine Noonan
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
| | - Peter M. Rabinowitz
- Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Mary J. Lancaster
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
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