Prototype • AI & verification hooks

Better preparedness through early situational awareness

A vision, not a guarantee. We hope that—with partner data and responsible governance—tools like this could help planners see respiratory pressure building earlier so facilities can rehearse readiness. Today there is no validated forecast for Nepal tied to operational facility data here; the dashboard illustrates the idea with synthetic patterns only.

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✅ Open-source (MIT)

✅ Verification hooks (demo)

✅ Model pipeline (not clinically validated)

What we are building toward

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Data-Ready

The platform is structured for DHIS2 and facility reporting. The live demo uses scientifically grounded synthetic respiratory patterns across eight Bagmati Province cities—not ministerial cohort counts tied to verified facilities.

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Models (future validation)

Forecasting would need training, evaluation, and sign-off on real partner data. The demo includes a simple illustrative trend helper only—it is not a certified clinical or public-health decision tool.

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Integrity & audit trails

We explore cryptographic anchoring and optional signing so that, when real reporting exists, exports can be reviewed for tampering. Nothing on this site constitutes a legal warranty or on-chain guarantee of health outcomes.

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Accessible UI

The dashboard is responsive. SMS or offline workflows would be part of a future phased rollout—not claimed as live here.

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Integration intent

We aim to align with common health information exchange patterns (e.g. DHIS2-style APIs). Any live linkage requires partner agreements, security review, and data agreements.

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Phased expansion (roadmap language)

Architecture conversations include initial deployment → wider geography → national readiness. Timelines and numbers are aspirations until funded, governed projects exist.

Aspirations—subject to evidence

From reacting after the spike to noticing pressure earlier

Surges strain services worldwide. Earlier situational awareness—when trustworthy data exists—can in principle give planners time to coordinate supplies and staffing. We are not asserting that burden here or that outcomes change without rigorous evaluation.

Hopes (not commitments): With partner data under proper oversight, shared signals might support coordination so facilities face surges less blind. Whether that materially reduces harm must be demonstrated; we intend to pursue that responsibly.

Earlier possible awareness for planners—only after secure reporting pipelines and agreed thresholds exist
Room to rehearse preparedness (oxygen, surge spaces, staffing) if warnings prove reliable—hypothesis, not outcome
Reduced severe caseload—unproven here and would need trial design and ethics review
Clearer information flow across levels—an implementation goal, not an automatic effect
Measurable impact—only after a defined rollout, approved indicators, and independent where possible

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What the demo shows today

The dashboard exercises the product: API, verification hooks, and charts using realistic synthetic weekly loads. It does not claim an approved facility list, population denominator, or ministry-validated deployment footprint.

8 cities — configuration for Bagmati-style demo only

Population covered — to be set with partners when DHIS2 (or equivalent) scope is agreed

Health facilities in scope — to be enumerated from that same agreement

Roadmap: phased rollout → expansion → national readiness (subject to governance and data access)

No warranty; not medical advice. This website and demo are illustrative. They do not provide clinical guidance, regulatory clearance, or guarantees of accuracy, timeliness, completeness, or effectiveness. Any discussion of health outcomes is speculative until tested with partner data and appropriate study design. The authors, contributors, funders, and hosts do not accept liability for decisions or damages arising from use of this material. Use at your own risk. We hope—with partners—to move from prototype to evidence responsibly.