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Vaccine. 2015 Jun 9;33(25):2858-61. doi: 10.1016/j.vaccine.2015.04.022. Epub 2015 Apr 18.

The Benin experience: How computational modeling can assist major vaccine policy changes in low and middle income countries.

Author information

1
Johns Hopkins School of Public Health, Baltimore, MD, USA; HERMES Logistics Modeling Team, USA. Electronic address: brucelee@jhu.edu.
2
Agence de Médicine Préventive (AMP), Paris, France.
3
HERMES Logistics Modeling Team, USA.
4
PATH, Seattle, WA, USA.
5
HERMES Logistics Modeling Team, USA; Pittsburgh Supercomputing Center (PSC)/Carnegie Mellon University, Pittsburgh, PA, USA.

Abstract

While scientific studies can show the need for vaccine policy or operations changes, translating scientific findings to action is a complex process that needs to be executed appropriately for change to occur. Our Benin experience provided key steps and lessons learned to help computational modeling inform and lead to major policy change. The key steps are: engagement of Ministry of Health, identifying in-country "champions," directed and efficient data collection, defining a finite set of realistic scenarios, making the study methodology transparent, presenting the results in a clear manner, and facilitating decision-making and advocacy. Generating scientific evidence is one component of policy change. Enabling change requires orchestration of a coordinated set of steps that heavily involve key stakeholders, earn their confidence, and provide them with relevant information. Our Benin EVM+CCEM+HERMES Process led to a decision to enact major changes and could serve as a template for similar approaches in other countries.

KEYWORDS:

Benin; Computational modeling; Supply chain; Vaccine

PMID:
25900134
PMCID:
PMC4623312
DOI:
10.1016/j.vaccine.2015.04.022
[Indexed for MEDLINE]
Free PMC Article

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