Smartphone-based retrospective analysis for malaria hotspot detection.

OBJECTIVE

To leverage passively collected retrospective smartphone location data to accurately identify malaria transmission hotspots through the development of a novel tool, the Sickness Positioning System.

METHODS

We 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.

RESULTS

Sickness 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.

CONCLUSION

Sickness 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.