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Updated: Feb 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Developing a community-centred malaria early warning system based on indigenous knowledge: Gwanda District, Zimbabwe
Margaret Macherera1,2, Moses J Chimbari2
1Department of Environmental Science and Health, National University of Science and Technology, Zimbabwe.
Abstract:
Malaria continues to be a major public health problem in Sub-Saharan Africa despite efforts that have been made to prevent and control the disease for many decades. The knowledge on prediction and occurrence of the disease that communities acquired over the years has not been seriously considered in control programmes. This article reports on studies that aimed to integrate indigenous knowledge systems (IKS) on malaria into the malaria control programme in Gwanda District, Zimbabwe. The studies were conducted over a 3-year period. Data were collected using participatory rural appraisals, key informant interviews, household interviews and workshops in three wards (11, 15 and 18) with the highest malaria incidence in Gwanda District. Disease livelihoods calendars produced by the community showed their knowledge on the relationship between malaria, temperature and rainfall, and thus an understanding of malaria as a hazard. Volunteer IKS experts willing to record the indigenous environmental indicators for the occurrence of malaria in the study area were identified by the communities. Indigenous environmental indicators for the occurrence of malaria were classified as insects, plant phenology, animals, weather and cosmological indicators. Plant phenology was emphasised more than the other indicators. A community-based malaria early warning system model was developed using the identified IKS indicators in two of the wards using the ward health team as an entry point to the health system. In the model, data on indicators were collected at the village level by IKS experts, analysed at ward level by IKS experts and health workers and relayed to the district health team.
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