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КыргызчаAI for Disease Vector Surveillance & Mosquito Control
Detect, map, and control mosquito-borne disease risk with AI-driven surveillance, climate signals, and operational playbooks.
Mosquito-borne diseases threaten public health. AI can fuse trap counts, climate, mobility, and land use to predict risk, target interventions, and monitor effectiveness. Outcomes: fewer cases, optimized spray/larvicide, and better community engagement.
Community SMS/app campaigns for behavior change.
Monitoring & Evaluation
Effectiveness tracking of sprays/larvicides vs cases/traps.
Trap image pipelines; EO/land use tiling; climate feeds.
Data quality: QC of trap images, geo/time alignment.
Privacy-aware case data ingestion; aggregation.
Frequently asked questions
What is the Implementation Roadmap?
Implementation Roadmap
How do I set up trap imaging and quality control, including baseline risk modeling, and should I pilot one region?
Set up trap imaging/QC; baseline risk model; pilot one region.
Once the risk maps and alerts are deployed, what targets should I be focusing on – specifically, what interventions should I implement, and how should I measure key performance indicators (KPIs)?
Deploy risk maps and alerts; target interventions; measure KPIs.
Should I incorporate Earth Observation (EO) water and land layers into the simulation, and can I automate tasking alongside community communications?
Adding EO data like water and land layers will enhance the simulation's realism, and automation of tasking combined with regular community communication will improve user engagement.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.