Mobile-first outbreak intelligence that replaces bureaucratic delays and fortress failures with proven, rapid response
International bureaucratic inertia and misguided "fortress" containment strategies allow Ebola to rapidly overwhelm African nations despite clear lessons from past outbreaks.
EpiSwift gives African field teams a mobile app for instant symptom reporting with offline sync. AI analyzes reports against every major past Ebola outbreak to recommend adaptive zoning instead of blanket fortress quarantines. Policymakers and aid organizations access a real-time dashboard with predictive spread models and one-click protocol deployment that bypasses slow approval chains.
Global health policymakers, African epidemic response teams, and international aid organizations managing infectious disease outbreaks
Only platform with a proprietary efficacy database from 2014-2022 Ebola responses that actively recommends and simulates smarter containment strategies proven to reduce overwhelm by 40%+
supportive
Field workers log symptoms, GPS, photos with full offline queuing and auto-sync
On-device ML model forecasts local spread and suggests non-fortress interventions
Delivers context-aware response protocols updated without central bureaucratic sign-off
Real-time outbreak map and analytics for teams and policymakers
Test different strategies against historical Ebola data to see outcomes
Encrypted chat, task assignment and escalation across local and global teams
Search past outbreaks by scenario with what-worked vs what-failed summaries
Speech-to-text in Swahili, French, Hausa and English
Predicts supply needs 7-14 days ahead based on trajectory
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| country | text | No |
| verified | bool | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| org_id | uuid | No |
| text | No | |
| role | text | No |
| phone | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| org_id | uuid | No |
| location | point | No |
| symptoms | jsonb | No |
| status | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| org_id | uuid | No |
| scenario_type | text | No |
| inputs | jsonb | No |
| results | jsonb | No |
| created_at | timestamp | No |
Relationships:
/api/reportsSubmit symptom report (offline capable via queue)
/api/predictionsGet AI risk predictions and recommended actions for a region
/api/protocolsFetch latest localized protocols
/api/simulateRun containment strategy simulation
/api/dashboardRetrieve live outbreak dashboard data
Max 50 reports per month per org
Up to 25 users per organization
Unlimited users and reports
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 140 | 9% | $441 | $5,292 |
| Month 6 | 720 | 14% | $3,528 | $42,336 |
Mobile outbreak intelligence that learns from every past African response. Replace fortress thinking with strategies that actually work.
Contact Africa CDC and West African Health Organization response leads via warm intros from LinkedIn offering free 90-day pilots in exchange for usage data. Attend or speak at next African Union health security meeting with a live demo using anonymized 2014-2022 data. Partner with MSF and International Rescue Committee field directors by solving one of their current coordination pain points in a customized workshop.
Excellent real-time tracking visualization
No mobile field reporting or actionable protocol engine
Combines tracking with mobile-first execution tools and simulation of better containment strategies
Strong AI risk modeling
Primarily serves Western governments and travel industry, not African field teams
Built specifically for low-connectivity African contexts with offline mobile capabilities
Massive institutional network
Suffers from the exact bureaucratic inertia the platform was built to solve
Automates what GOARN does manually and provides data-driven alternatives to failed strategies
Network effects from accumulating anonymized response efficacy data across African health organizations, continuously improving the AI simulator and recommendations
Recent Sudan and Uganda Ebola outbreaks plus massive post-COVID preparedness funding have created urgent demand. Edge ML and affordable smartphones now make sophisticated tools viable in rural health posts.
Cross-border health data privacy compliance (GDPR + African data laws)
Anonymize at source, implement data residency per country, work with local ministries as co-stewards
Entrenched bureaucracy resisting new tools
Start with enthusiastic early adopters (MSF, local NGOs) and use their success stories to create pull from governments
ML model accuracy in new outbreak variants
Hybrid approach (ML + human expert override) and continuous retraining with new reports
Success: ≥75% say they would pay $35+/mo and schedule pilot
Success: ≥70% retention and ≥40% report faster decision making
Success: 150 signups and 12 paid conversions in first 30 days
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