The African Institute for Mathematical Sciences-Next Einstein Initiative (AIMS NEI) and the Malaria Modelling for Sustainable Public Health Policies in Africa (MaModAfrica) consortium through funding by Bill and Melinda Gates Foundation is set to roll out Master’s and PhD programmes in malaria modelling in September and October 2023, respectively. Malaria modelling refers to the process of using mathematical models to study the transmission dynamics and control of malaria. The goal is to understand how the disease spreads through populations and to identify the most effective ways to control or eliminate it. AIMS is leading this ambitious initiative in collaboration with reputable universities, research institutions, operational partners, and a spectrum of National Malaria Control Programs (NMCPs) from Benin, Rwanda, Senegal, Burkina Faso, Mozambique, Côte d’Ivoire, Switzerland, Australia, Kenya and Ghana that make up the MaModAfrica consortium. The project aims to increase the number of mathematically trained malaria experts in sub-Saharan Africa through various educational programmes and workshops. It also aims to bridge the gap between academic modelling and public health needs by providing evidence-based solutions, in addition, the initiative also aims to establish a sustainable network of African disease modellers by promoting academic exchange among consortium members. During the MaModAfrica consortium’s first in-person meeting held from May 1 to May 5, Prof Wilfred Ndifon, AIMS Chief Scientific Officer and Principal Investigator for MaModAfrica, explained that the malaria models developed through the project will provide projections for future scenarios, and help understand the impact of interventions on natural processes. This is particularly significant because malaria is a noteworthy cause of mortality in children under the age of five (5) as well as mobility issues in the elderly population. “We want to understand the current state of affairs, the impact of malaria on populations, and its future implications in light of changes in climate, environment, and demographics,” said Ndifon, adding that they also aim to investigate how these changes could affect the number of malaria cases. Ndifon further noted that by using malaria models, they will be able to determine the impact of interventions such as bed nets and Indoor residual spraying (IRS) on the disease burden, and assess the potential increase in malaria cases if they were to stop these interventions or the potential decrease if they intensify. Ndifon further reiterated the commitment to train young people to support national malaria control programmes (NMCPs) in various countries, especially Benin, Rwanda, and Mozambique, with the aim of reducing the disease burden and eventually eradicating malaria. At the conclusion of the meeting, the consortium members are expected to finalise the curricula for the Master’s and PhD programmes in malaria modelling, which they have been developing since September 2022. They will also have final touches on the student selection processes for both programs. Rwanda Biomedical Centre (RBC), which was present at the meeting, has reported a significant decline in malaria incidence from 409 cases per 1,000 people in 2017 to 76 cases in 2022, which represents an 81 per cent decrease. Eric Remera, an epidemiologist researcher at RBC, recognises the achievement of reducing malaria incidence rates. He notes that malaria modelling will serve as an additional data science intervention to help identify gaps and predict trends that can guide future and more efficient prevention efforts. Remera explained that the mathematical modelling approach will use historical data and geographical information to predict when and where malaria is likely to occur, with more precise predictions at the grassroots level. Dr Emilie Pothin, a malaria modeller for the Swiss Tropical and Public Health Institute, stated that they initially focused on countries with the highest burden of malaria, but are now expanding their efforts to include low-burden countries such as Rwanda as means of sharing best practices as well as endeavouring to understand the underlying factors that contribute to malaria endemicity (malaria intensity and prevalence in a particular region) and heterogeneity (diverse in nature[FM1] ) in Rwanda thereby developing appropriate tools to respond to it. This, she said, is advantageous as malaria endemicity in Rwanda is quite heterogeneous, and mathematical models can aid in understanding the underlying factors that contribute to this stratification and developing appropriate tools to respond to it. Pothin noted that they will assess the most impactful interventions for every dollar spent, emphasising that the focus should be on impact and efficiency.
