Data Schema
MosqAI data models combine field events, species inference, climate overlays, and intervention metadata into one lineage-aware record system.
Workflow
Capture and normalize
Field telemetry, image frames, weather feeds, and site metadata are normalized into one event fabric.
Score and enrich
MosqAI enriches the event with model outputs, thresholds, contextual overlays, and governance metadata.
Publish operational action
Dashboards, alerts, maps, and exports update so analysts and field crews can act from the same signal.
Schema philosophy
MosqAI models are built around one idea: a mosquito signal is only as useful as the context attached to it. The schema therefore preserves where an event happened, what environmental conditions surrounded it, how the platform interpreted it, and what teams did next.
- Events keep operational and ecological context together
- Zones act as first-class reporting units
- Intervention history remains linked to later analysis
Primary entities
The schema revolves around traps, observations, alerts, zones, interventions, and exports. Each exists independently but is designed to join cleanly so a downstream consumer can reconstruct a full operational narrative.
- Trap records describe physical deployment and health
- Observation records capture interpreted field events
- Alert records formalize operational significance
- Intervention records describe what changed in the field
Lineage fields matter
A record without lineage is useful only once. MosqAI preserves source timestamps, enrichment metadata, and cross-references so analysts can revisit decisions later and explain why the platform concluded what it did.
- Keep ingestion, observation, and publication times separate
- Record model version or scoring context when appropriate
- Preserve references to upstream climate or environmental sources
Export behavior
The schema is designed to survive export. Even when data leaves the platform for GIS, BI, or scientific analysis, it should still carry enough context to be understandable outside MosqAI.
- Avoid flattening away zone or lineage context
- Preserve intervention linkage where possible
- Include enough metadata for later reproducibility
Frequently asked questions
Is data schema only useful for large programs?
No. Pilot deployments often get the fastest value because they replace fragmented observation with a single system of record and shorten the loop between field events and action.
Does it require continuous connectivity?
No. MosqAI is designed for variable field connectivity and can buffer, sync, and reconcile data when coverage returns.
How does it fit with existing reporting?
The platform is built to complement GIS, public-health, and contractor workflows through exports, APIs, and shared evidence views rather than forcing teams to discard current systems immediately.