Surveillance and control of malaria at the local level using e-health platforms – SMALLER

Despite control efforts, malaria still has a devastating impact on the well-being of millions of people, especially in rural areas of sub-Saharan Africa. Low healthcare access, weak health systems, and a decline in the efficacy of vector control interventions threaten to reverse the progress made in the last two decades. In this sense, community health workers (CHWs) are gaining an ever increasing role in the Global Malaria Strategy as a solution to ensure universal access to malaria diagnosis and treatment, and to improve surveillance. However, to optimize the effectiveness of malaria control strategies, local implementation should be informed by a comprehensive understanding of context-specific drivers of malaria dynamics. In particular, the level of granularity and timeliness of data that e-health platforms at the community level can offer opens new possibilities for malaria control that are still largely unexplored. Integration of feedback loops between infectious disease modelling approaches and existing community-based e-health surveillance platforms could help to 1) target efforts and plan resources necessary ahead of time for specific areas and periods, reducing stock-outs and increasing case detection; and 2) implement additional control activities that are predicted to minimize transmission at the population level. However, while malaria modelling is increasingly informing national or regional planning, their application at the local level, where intervention efforts actually take place, remains scarce. This is especially true for rural areas of sub-Saharan Africa, where the burden is highest.

The goal of this project is to develop statistical and mathematical models of malaria transmission that will inform key features of program implementation, helping to optimize surveillance and control strategies at the community level. For this, we propose coupling for each community in the district of Ifanadiana (south-eastern Madagascar), accurate epidemiological surveillance of malaria cases, high resolution satellite environmental information, and longitudinal socio-economic and behavioural data. The project will be developed in close partnership with the healthcare NGO PIVOT, who works since 2014 with the Ministry of Health in Ifanadiana to combine accessible and comprehensive healthcare services with rigorous scientific research, investing in robust data collection systems for program implementation. Together, we pilot innovative and reliable malaria decision-making tools that can be validated locally and scaled-up to other rural areas.

Specifically, this project aims to 1) estimate the unobserved burden of malaria at the community level by facility-based passive surveillance; 2) integrate community-level predictions of malaria transmission into existing CHW workflows in Ifanadiana district for improved program implementation; and 3) inform implementation of additional control strategies that minimize malaria transmission through transmission model simulations. Impact assessments will be carried out to evaluate whether the availability of such modelling outputs helps improve key indicators of malaria program performance. Besides its scientific novelty, this project will represent an exemplary case for cross-sectoral collaboration between researchers and civil society (NGOs, local governments) for improving population health in low-resource settings.