nipahwatch.com aggregates Nipah virus outbreak signals and other zoonotic spillover events from WHO alerts, ProMED Mail, and local health ministry reports. AI-parsed for relevance, manually verified for accuracy, geo-mapped for analytical context. Built for epidemiologists, public health analysts, and OSINT researchers tracking emerging viral threats.
nipahwatch ingests zoonotic disease reports from World Health Organization alerts, ProMED Mail dispatches, and official local health ministry releases. The AI parsing layer extracts case counts, geographic location, fatality data, and confirmation status. Every entry is then manually reviewed before publication. The platform makes a clear distinction between confirmed laboratory-verified cases and suspected clusters pending confirmation, and it links every entry to its primary source.
The platform is positioned explicitly as an independent OSINT tool, not a substitute for official health authority guidance. Methodology and source attribution are exposed on every page. Raw surveillance data is available as a downloadable CSV for researchers who need it in their own analytical pipelines.
WHO Disease Outbreak News for top-tier alerts, ProMED Mail for early epidemiological signals, official local health ministries for in-country confirmation. Each signal is tagged with its source tier.
Initial parsing extracts case counts and geographic data automatically. Manual verification confirms accuracy before publication. The methodology is documented openly so users understand the verification chain.
Lab-verified cases and suspected clusters are tracked as distinct categories. Case Fatality Rate is computed per confirmed cohort, not blended. Distinction matters for epidemiological analysis.
Leaflet-powered map with active outbreak, suspected cluster, and historical monitoring layers. Visual scan reveals geographic clustering and spread patterns.
Country-level breakdown with confirmed cases, suspected cases, deaths, CFR, and current status. Sortable, exportable, structured for downstream use.
The full surveillance dataset is published as a downloadable CSV via Google Sheets. Researchers can pull the raw data into their own analytical environments without scraping the site.
Subscribers receive notifications when a confirmed Nipah cluster is identified. Notification scope is intentionally narrow to avoid alert fatigue.
The platform documents itself clearly as an independent OSINT tool, not an official health authority. Users are directed to defer to local health authorities for clinical decisions.
Every page links back to authoritative sources (WHO, ProMED) so users can trace any claim to its origin. No information appears without a verifiable provenance trail.
Public health surveillance is an underserved category for independent platforms. WHO publishes alerts at official cadence. ProMED runs subscriber-only dispatches. Local ministries publish in their own languages and formats. nipahwatch sits at the intersection: aggregating, translating, geo-mapping, and standardizing this fragmented signal layer for analysts who need a single defensible view.
The architecture is deliberately conservative. Two-stage verification (AI parse plus manual review), explicit source attribution on every entry, clear methodology documentation, and a stated boundary that the platform is not a substitute for official guidance. The discipline is what makes the data trustworthy enough to be useful in a real epidemiological workflow.
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