Epidemiological modelling for public health decision making in sub-Saharan Africa

This project explored what is required to strengthen national level modelling capacity in sub-Saharan Africa (SSA) to improve the capacity of countries to respond to future epidemics and to enable them to tackle endemic disease burdens more effectively. Here, modelling capacity is understood to include the capacity of local researchers to generate models, the capacity of policymakers to use them for decision making, and the effectiveness of communication and collaboration between these two communities. The project conducted case studies in South Africa, Kenya and Ghana.

We drew together the findings from these case studies to construct a conceptual framework that can be used to further develop capacity strengthening strategies. This framework is intended to inform decision making by governments, donors, research funders, and organisation implementing capacity strengthening interventions.

Challenges

The Covid-19 pandemic raised global awareness of disease modelling among policymakers and the general public. Models were used to inform public health decisions that profoundly affected the lives of billions of people worldwide. The pandemic also highlighted substantial differences in the capacity to conduct disease modelling between countries, and particularly the apparent lack of such capacity in many low- and middle-income countries (LMICs).

In response to this, significant efforts were made by research organisations in high-income countries (HICs) to provide modelling results relevant to LMICs. However, data limitations and the generalised nature of globally produced models weakened their utility and raised concerns over their accuracy. The availability of high-quality modelling evidence to inform decisions and the capacity of policymakers to use this evidence is therefore critically important. Additionally, modelling expertise is not only of value in epidemic contexts, but can play an important role in the management of endemic disease.

Our approach

Case study countries were selected to explore regional variation across Africa and different levels of existing capacity and experience with generating and using mathematical disease modelling to inform decision making. The primary source of information for these case studies was key informant interviews with relevant stakeholders, ranging from key policymakers to early career researchers.

Outcomes

This framework emphasises several factors:

• capacity strengthening efforts should begin with a detailed analysis of current circumstances across the research–policy ecosystem;
• capacity strengthening should involve a coordinated package of interventions, potentially requiring collaboration between multiple funding organisations that aim to achieve a sustainable shift in the national research–policy ecosystem; and
• these packages of interventions will usually need to target multiple levels, including individual skills and organisational capabilities, as well as the connectiveness and coherence of the research–policy ecosystem.

The framework is already being using by research funding organisations to inform and coordinate capacity strengthening efforts.