Efficient systems for early detection,prevention,and management of livestock diseases and animal health issues
Government Of Maharashtra · Agriculture, FoodTech & Rural Development · Software
Build the spatiotemporal clustering and the IVR reporting path properly and treat everything else as supporting — those two are the only parts that address the failure the description actually describes.
What it actually is
Animal disease outbreaks get noticed late because farmers report symptoms slowly, labs are far away, and records from farms, dispensaries and vaccination drives never come together. The ask is a surveillance system that captures reports from the field, flags likely outbreaks early, and coordinates the response — working even where connectivity is poor.
What to build
A surveillance and response platform where farmers and field workers report symptoms and mortality through mobile, IVR or offline-capable forms in their own language, a triage layer scores each report and clusters reports in space and time to flag a suspected outbreak before any single case is confirmed, a geospatial view maps clusters against historical disease patterns and weather, animal or herd-level vaccination and treatment records are maintained and linked to reports, and a veterinary official dashboard escalates a flagged cluster into sample collection, laboratory referral and containment actions with multilingual advisories pushed back to the affected villages.
Smallest thing that wins the room
Submit three separate symptom reports from neighbouring villages over two days by IVR and watch the system cluster them into a single suspected outbreak, map it, escalate it to the district veterinary officer with a referral request, and push a multilingual advisory back to the reporting villages.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 5% of the 226 · #214 of 226 by expected field · reaches the 500 cap
Forecast to blow past the 500-idea cap. Submissions close when it fills, so late teams may not get in at all.
Why: state governments drew the biggest crowds in 2025.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
3/5The spatiotemporal outbreak clustering is a genuinely defensible core and the IVR path addresses a real access barrier, but the description's nine-item feature list invites a shallow everything-platform and it sits among seven similarly broad Maharashtra statements a judge will see in sequence.
Feasibility
4/5Reporting forms, spatiotemporal clustering, an IVR path and dashboards are all standard engineering with no data dependency — the outbreak clustering can run on rule-based spatial proximity without needing any trained model.
Innovation scope
3/5The description enumerates the components it expects, so the product shape is largely given, and your room is in the triage logic and how you make reporting work for a farmer with a feature phone.
Clarity
3/5The Expected Solution lists nine capabilities from symptom capture through laboratory referral to dashboards without prioritising any, so a team knows the breadth but not which part is actually being assessed.
Effort
MassiveFarmer reporting across mobile, IVR and offline channels, clustering, records management, advisories, referral workflow and official dashboards is six or seven components rather than one product.
Demo-ability
MediumThe reports-clustering-into-an-outbreak moment is a good story, but much of the platform is administrative workflow that does not carry visual impact.
In its favour
- Green flag: Scan-statistic outbreak clustering is a well-established epidemiological method, so your detection logic rests on published technique rather than an invented heuristic
- Green flag: IVR reporting genuinely reaches farmers without smartphones, which is the actual barrier the description names and which most teams will ignore
- Green flag: The system needs no training data at all — clustering works on the reports themselves from day one
- Green flag: Zoonotic risk gives the impact case real weight with a public health judge
Against it
- Red flag: Nine listed capabilities is a kitchen sink, and a team that builds all of them shallowly will lose to one that made the clustering and IVR genuinely work
- Red flag: Farmer-reported symptoms are noisy and non-specific, so your clusters will contain many false alarms and the triage precision is what a veterinary judge will probe
- Red flag: It overlaps SIH26109 on livestock disease prediction, so be clear that this is population surveillance rather than individual animal prediction
- Red flag: The system depends on farmers actually reporting, and no software feature solves the incentive problem behind under-reporting
What you will be writing
- Spatiotemporal cluster detection (SaTScan-style scan statistics)
- Twilio / Exotel IVR symptom reporting
- PostGIS outbreak mapping
- Offline-first mobile capture with sync
- Bhashini multilingual advisory generation
- React veterinary official dashboard
- Animal health surveillance
- Outbreak detection
- Rural service delivery
Prior art to read before you start
syndromic disease surveillance · spatiotemporal outbreak clustering · low-connectivity field reporting
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.