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Nansubuga, B., et al. (2026) Barriers and Facilitators to the Use of DHIS2 Data for Malaria Surveillance in Uganda. PubMed Central (PMC).

has been cited by the following article:

• TITLE: Geospatial Mapping of Malaria Commodity Mismatches for Targeted Redistribution Hotspot Identification in Uganda

  AUTHORS: Maria Assumpta Komugabe, Itamar Shabtai, Moses Kizito, Simon Kong, Richard Caballero

  KEYWORDS: Malaria, Supply Chain Resilience, Spatio-Temporal Analysis, Emerging Hot Spot Analysis, Artificial Intelligence, Commodity Redistribution, Uganda, Geospatial Intelligence, Inventory Mismatch, Public Health Logistics

  JOURNAL NAME: Journal of Geographic Information System, Vol.18 No.3, June 3, 2026

  ABSTRACT: Malaria remains a critical public health challenge in Uganda, marked by significant subnational transmission heterogeneity. This study analyzed 260 weekly surveillance reports (January 2020-December 2024) to quantify geographic imbalances in artemisinin-based combination therapies (ACTs) and rapid diagnostic tests (RDTs). Emerging Hot Spot Analysis (EHSA) revealed a “mismatch contradiction”: while ACT understocking trended downward by late 2024, RDT deficits remained tenfold higher. Longitudinal analysis identified 31 districts (23%) as Sporadic Cold Spots of chronic oversupply, while Persistent Hot Spots of understocking were concentrated in the Central region. These findings indicate that manual logistics are insufficient to rectify geographic maldistribution. We recommend an integrated AI-GIS redistribution framework to facilitate real-time, demand-driven commodity transfers. These findings indicate that traditional manual logistics frameworks are insufficient to rectify geographic maldistribution. This study advocates for a digital transformation of the supply chain, proposing an integrated AI-GIS redistribution framework to facilitate real-time, demand-driven commodity transfers.