ArticleEinstein (Sao Paulo, Brazil)2025
Smartphone-based retrospective analysis for malaria hotspot detection.
Article in Einstein (Sao Paulo, Brazil), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo leverage passively collected retrospective smartphone location data to accurately identify malaria transmission hotspots through the development of a novel tool, the Sickness Positioning System.
methodsWe collected anonymized location history data from over 200 malaria-infected individuals using Google Takeout. Sickness Positioning System applies density-based clustering techniques to this passively collected data to identify areas of elevated infection risk. The identified hotspots were subsequently validated through field investigations.
resultsSickness Positioning System successfully pinpointed multiple areas of increased malaria transmission risk. Field validation confirmed two previously unrecognized mosquito breeding sites as active sources of transmission. When compared to Brazil's federal surveillance system - which updates data annually - Sickness Positioning System provided a more precise and timely spatial assessment of malaria spread.
conclusionSickness Positioning System demonstrates a cost-effective, objective, and timely method for identifying malaria transmission hotspots. This approach has the potential to significantly enhance disease surveillance and facilitate more targeted public health interventions.
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