Lessons learned from COVID-19 modelling efforts for policy decision-making in lower- and middle-income countries
We provide a contextualised understanding of knowledge translation for LMICs during the COVID-19 pandemic. Furthermore, we share key lessons on how knowledge translation from mathematical modelling complements the broader learning agenda related to pandemic preparedness and long-term investments in evidence-to-policy translation. Our findings led to the co-development of a knowledge translation framework useful in various settings to guide decision-making, especially for public health emergencies.
Authors
- So Yoon Sim (ORCID: https://orcid.org/0000-0002-3267-1867)
- Fatuma Guleid (ORCID: https://orcid.org/0000-0002-8103-8136)
- Joyline Jepkosgei (ORCID: https://orcid.org/0000-0002-0489-3710)
- Collins J. Owek (ORCID: https://orcid.org/0000-0001-8419-7282)
- Brittany Hagedorn (ORCID: https://orcid.org/0000-0002-1489-7168)
- Jacquie Oliwa (ORCID: https://orcid.org/0000-0002-4575-2447)
- Jacinta Nzinga (ORCID: https://orcid.org/0000-0001-8394-8857)
- Raymond Hutubessy (ORCID: https://orcid.org/0000-0002-9286-5065)
- Justinah Maluni (ORCID: https://orcid.org/0000-0003-2079-7283)
- Vincent Were (ORCID: https://orcid.org/0000-0002-3554-795X)
Institutions
- University of Nairobi (KE)
- Gates Foundation (US)
- Kenya Medical Research Institute (KE)
- World Health Organization (CH)
- African Population and Health Research Center (KE)
Publication Details
- Journal
- BMJ Global Health
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1136/bmjgh-2024-015247
- Citations
- 8
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 3.16
Funders
- Bill and Melinda Gates Foundation