Lessons learned from COVID-19 modeling efforts and impact on policy in lower- and middle-income countries (LMICs)
Background and justification: There have been a multitude of modeling frameworks used in different
ways and settings to advise the various pandemic response policy decisions. However, not all evidence
was adequate, effectively communicated, or used by policy makers-likely resulting in missed
opportunities, wasted time and effort, and loss of life. The know-do gap (knowledge translation) is likely
a bottleneck to rapid, effective policy decisions, especially crucial in emergency situations. As part of
pandemic preparedness, it is necessary to have decision support systems in place to support rapid and
effective responses. To ensure this is done well, there is need to understand how modeling and
analytical methods can rapidly be made available and fully integrated into decision making processes.
Objectives: Guided by Graham’s knowledge to action framework, we aim to identify best practices and
enabling environmental factors, gather lessons learned on how to redeploy existing technical capacity,
and understand what infrastructure is needed for knowledge transfer and exchange to be successful, for
the ongoing pandemic and future emergencies.
Methods: We shall use an exploratory (case study) approach, using methods including online survey, key
informant interviews and participatory learning workshops to understand the knowledge creation and
exchange processes between researchers and policy makers during the pandemic, with a focus on lower
and middle-income countries, sampled from sub-Saharan Africa, South-East Asia and Latin America.
Significance of results: Pandemic preparedness is on the forefront of policy makers and funders’ minds
currently. Modeling has played a substantial role in shaping both the public’s understanding of the
pandemic and informing difficult choices on policy trade-offs. We need to leverage this attention to
establish new ways of working in pandemic preparedness capacity for the future. In order to do so
effectively, we must learn from what has (not) worked during the current COVID-19 crisis.
Problem Statement:
During the COVID-19 pandemic, there was extensive modeling and analytical work done to support
policy decisions. However, not all modeling and evidence was adequate, effectively communicated, or
used by policymakers. This likely resulted in substantial missed opportunities, wasted time and effort,
and ultimately loss of life during the COVID-19 pandemic. The know-do gap (also known as knowledge
translation) is a bottleneck to rapid, effective policy decisions, especially in emergency situations, and
more so in LMICs that have strained health systems. There is growing interest in knowledge to action to
avoid research waste. It is important to understand how policy makers have been trying to use modeling
data for their decision making, what challenges they face, and how things can be made better. It is also
important to understand from the researchers’ perspectives what challenges they have/have had in
communicating their research findings, especially those of complex modeling outputs to policy makers.
And how they both can be better supported to ease the knowledge transfer process.
What questions are we trying to answer?
The aim of this work is to identify best practices and enabling environmental factors, gather lessons
learned on how to redeploy existing technical capacity, and understand what infrastructure ((i.e., human
resources and expertise, modeling tools and supporting IT systems as well as decision making bodies) is
needed to be successful.
Sites where the project is based
- Africa
- Latin America
- Southeast Asia