National Malaria Indicator Dashboard

This project was designed as a national malaria reporting dashboard for exploring health indicators across multiple organisation-unit levels in Nigeria. It gives users a practical way to move from high-level trends to more granular operational views without leaving the same application.

Key features include:

• Real-time DHIS2 integration through authenticated API calls to pull malaria analytics data directly from the Nigeria DHIS2 instance.
• A structured indicator mapping system that links raw DHIS2 data elements and category option combos to human-readable malaria indicators.
• Coverage across 123 indicators spanning prevention, diagnosis, treatment, impact, ANC, attendance, admissions, positivity, fever, severe malaria, and confirmed cases.
• A hierarchical organisation-unit selector that supports drilling from federal level down to states, LGAs, wards, and facilities.
• Lazy-loaded org-unit tree expansion so deeper nodes are fetched only when needed, improving performance and keeping the UI responsive.
• Flexible filtering by indicator, category, year, month, org-unit level, and organisation-unit group.
• Auto-sync on application load plus scheduled periodic refresh, so the dashboard can stay current without manual data reloads.
• Multiple analytics views including:
  • trend charts over time
  • location comparison views
  • top-location bar charts
  • summary tables
  • full raw-data tables
• Theme-based tabs for Prevention, Diagnosis, Treatment, and Impact, each generating focused chart grids for domain-specific monitoring.
• Axis switching that lets users compare time across locations or compare locations across time depending on the question they want to answer.
• Graceful fallback behavior with mock data and fallback hierarchy logic when live files or APIs are unavailable, which helps during development and deployment.
• Branded UI with NMEP/FMoH visual identity, custom CSS, and custom JavaScript for a more polished dashboard experience.