Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease Article Swipe
Donovan Guttieres
,
Charlot Diepvens
,
Catherine Decouttere
,
Nico Vandaele
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/vaccines12010024
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/vaccines12010024
Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018–2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research.
Related Topics
Concepts
Preparedness
Ebola virus
Context (archaeology)
Leverage (statistics)
Vaccination
Risk analysis (engineering)
Outbreak
Computer science
Business
Environmental health
Operations research
Environmental resource management
Geography
Medicine
Virology
Economics
Engineering
Management
Archaeology
Machine learning
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/vaccines12010024
- https://www.mdpi.com/2076-393X/12/1/24/pdf?version=1703576070
- OA Status
- gold
- Cited By
- 7
- References
- 128
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390231258
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390231258Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/vaccines12010024Digital Object Identifier
- Title
-
Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus DiseaseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-25Full publication date if available
- Authors
-
Donovan Guttieres, Charlot Diepvens, Catherine Decouttere, Nico VandaeleList of authors in order
- Landing page
-
https://doi.org/10.3390/vaccines12010024Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-393X/12/1/24/pdf?version=1703576070Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-393X/12/1/24/pdf?version=1703576070Direct OA link when available
- Concepts
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Preparedness, Ebola virus, Context (archaeology), Leverage (statistics), Vaccination, Risk analysis (engineering), Outbreak, Computer science, Business, Environmental health, Operations research, Environmental resource management, Geography, Medicine, Virology, Economics, Engineering, Management, Archaeology, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
128Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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