LESSONS LEARNED FROM COVID-19 MODELLING EFFORTS FOR POLICY DECISION-MAKING IN LOWER- AND MIDDLE-INCOME COUNTRIES

Abstract

AbstractIntroductionThe COVID-19 pandemic had devastating health and socio-economic effects, partly due to mitigating policy choices. There is little evidence of approaches that guided policy decisions in settings that had limited modelling capacity pre-pandemic. We sought to identify knowledge translation mechanisms, enabling factors, and structures needed to translate modelled evidence to policy decisions effectively.MethodsWe utilised convergent mixed methods in a participatory action approach, with quantitative data from a survey and qualitative data from a scoping review, in-depth interviews, and workshop notes. Participants included researchers and policy actors involved in COVID-19 evidence generation and decision-making. They were mostly from lower-and middle-income countries (LMICs) in Africa, Southeast Asia, and Latin America. Quantitative and qualitative data integration occurred during data analysis through triangulation and during reporting in a narrative synthesis.ResultsWe engaged 147 researchers and 57 policy actors from 28 countries. We found that the strategies required to use modelling evidence effectively include capacity building of modelling expertise and communication, improved data infrastructure, sustained funding, and dedicated knowledge translation platforms. The common knowledge translation mechanisms used during the pandemic included policy briefs, face-to-face debriefings, and dashboards. Some enabling factors for knowledge translation comprised solid relationships and open communication between researchers and policymakers, credibility of researchers, co-production of policy questions, and embedding researchers in policymaking spaces. Barriers included competition among modellers, negative attitude of policymakers towards research, political influences and demand for quick outputs.ConclusionOur findings led to the co-development of a knowledge translation framework useful in various settings to guide decision-making, especially for public health emergencies. Furthermore, we provide a contextualised understanding of knowledge translation for LMICs during the COVID-19 pandemic. Finally, 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.What is already known on this topicThere has been a multitude of modelling frameworks used in diverse ways to advise the various pandemic responses the world over, to an extent not seen before in public health.However, it is likely that not all modelling and evidence was adequate, effectively communicated, or used by policymakers.This is especially of concern in many LMICs that had strained health systems and resource constraints pre-pandemic.What this study addsThe know-do gap is a bottleneck to rapid, effective policy decisions, especially crucial in emergencies.As part of pandemic preparedness, it is necessary to have decision support systems in place.To ensure this is done well, there is a need to understand how modelling and analytical methods can rapidly be made available and fully integrated into decision-making processes.How this study might affect research, practice, or policyThis study contributed to the co-development of a knowledge translation framework that will be useful in building model-to-policy systems that can be adapted for use in various settings.We identified mechanisms required to strengthen knowledge translation in LMICs, and this complements the broader learning agenda related to pandemic preparedness and long-term investments in evidence-to-policy translation.