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Delport D, Sanderson B, Sacks-Davis R, Vaccher S, Dalton M, Martin-Hughes R, Mengistu T, Hogan D, Abeysuriya R, Scott N. A Framework for Assessing the Impact of Outbreak Response Immunization Programs. Diseases 2024; 12:73. [PMID: 38667531 PMCID: PMC11048879 DOI: 10.3390/diseases12040073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.
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Affiliation(s)
- Dominic Delport
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ben Sanderson
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
| | - Rachel Sacks-Davis
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Stefanie Vaccher
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
| | - Milena Dalton
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
| | - Rowan Martin-Hughes
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
| | - Tewodaj Mengistu
- Gavi, The Vaccine Alliance, 1218 Geneva, Switzerland; (T.M.); (D.H.)
| | - Dan Hogan
- Gavi, The Vaccine Alliance, 1218 Geneva, Switzerland; (T.M.); (D.H.)
| | - Romesh Abeysuriya
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Nick Scott
- Burnet Institute, Melbourne, VIC 3004, Australia; (B.S.); (R.S.-D.); (S.V.); (M.D.); (R.M.-H.); (R.A.); (N.S.)
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
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Azam JM, Pang X, Are EB, Pulliam JRC, Ferrari MJ. Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19. Epidemics 2023; 45:100720. [PMID: 37944405 DOI: 10.1016/j.epidem.2023.100720] [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: 08/22/2022] [Revised: 07/01/2023] [Accepted: 10/02/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination. METHODS We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices. RESULTS Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation. CONCLUSION The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.
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Affiliation(s)
- James M Azam
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom; DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa.
| | - Xiaoxi Pang
- Department of Mathematics, The University of Manchester, Manchester, United Kingdom
| | - Elisha B Are
- DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa; Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada
| | - Juliet R C Pulliam
- DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa
| | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, USA
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Quiner C, Jones K, Bobashev G. Impacts of timing, length, and intensity of behavioral interventions to COVID-19 dynamics: North Carolina county-level examples. Infect Dis Model 2022; 7:535-544. [PMID: 35992738 PMCID: PMC9374497 DOI: 10.1016/j.idm.2022.08.002] [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: 02/11/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 10/28/2022] Open
Abstract
We sought to examine how the impact of revocable behavioral interventions, e.g., shelter-in-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics in Wake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (β). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events.
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Affiliation(s)
- Claire Quiner
- RTI International, 3040 E. Cornwallis Road, PO Box 12194, Research Triangle Park, NC, 27709, USA
| | - Kasey Jones
- RTI International, 3040 E. Cornwallis Road, PO Box 12194, Research Triangle Park, NC, 27709, USA
| | - Georgiy Bobashev
- RTI International, 3040 E. Cornwallis Road, PO Box 12194, Research Triangle Park, NC, 27709, USA
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