National Health Intelligence
National Console · Malaysia · KKM
Threat levelElevated
Synced
14:02 MYT
Districts ranked by projected signal for . Tap to inspect.
State-wide burden, all tracked conditions. ● communicable · ● non-communicable · ● maternal
State-space sequence model · solid = observed, dashed = projected, band = 80% interval
This week vs trailing 12-week average
Indicator- + event-based signals across 16 states & federal territories
The map on the Overview tab is the same instrument. Here, signals are ranked across all conditions so a planner sees the single highest-priority intervention per district.
Per-condition projection with resource implication
Top districts by forecast increase · selected condition
Demand and capacity, state-wide
Bed & ICU occupancy by district cluster
Who is being reached - and who is not
Confidence in the synthesis · lower = sparser feeds, interpret with caution
Demonstration only. All figures are synthetic and illustrative - generated for interface design, not drawn from live surveillance. State & federal-territory names reflect Malaysia's 16-jurisdiction structure; spatial patterns are plausible (dengue weighted to the urban Klang Valley, zoonotic malaria to Sabah/Sarawak forest fringes, NCDs to urban and older populations, maternal anaemia to interior districts) but are not real case data. A national build would fuse routine registries (DOSM vital statistics, NHMS survey rounds), public surveillance (CPRC, iDengue, MOH HMIS facility reporting, MySejahtera) and granular MyZoyel clinic encounters - symptoms, investigations, diagnoses and prescriptions - with the State-Space sequence engine providing longitudinal trajectory and the validation layer cross-checking against the Malaysian Health Data Warehouse (MyHDW). The Data completeness map metric exists precisely because historical and private-sector data are patchy; the system should show that uncertainty rather than hide it.