Primary view currently driving the atlas state.
IHME Intelligence Prototype
This prototype turns the IHME ecosystem into a single visual story: where asbestos-related diseases hit hardest, where exposure-linked burden persists, and where the world is still under-observed. The current dataset is a synthesis scaffold designed to accept official IHME exports from GBD Results, GBD Sources, and related visualization tools.
Primary view currently driving the atlas state.
Countries with evidence-gap score above the global alert threshold.
Population living outside the inferred reporting coverage represented here.
Country combining high burden intensity with thin source support.
What is official
Disease burden structure is modeled on IHME GBD Results outputs and source metadata patterns from the GBD Sources Tool.
What is inferred
The evidence coverage score and uncovered-population estimate are prototype metrics derived from source count, recency, uncertainty, and population exposure.
What this becomes
A production-grade atlas once official IHME exports are dropped into the same JSON schema.
Global Atlas
Switch the disease lens, choose the map mode, and compare the world’s burden against the strength of the observed evidence underneath it.
World Map
Country Spotlight
Click any mapped country to see its burden pattern, source environment, and likely weak points in the evidence chain.
Score help
Evidence Coverage Score
Why this country matters
The atlas will surface a narrative here based on burden trajectory, source support, and uncovered population risk.
Signal Space
Upper-left is the danger zone: high burden with low evidence coverage. Bubble size reflects uncovered population.
Blind Spot Ranking
This ranking is intentionally ruthless: it prioritizes burden, uncertainty, recency, and the share of population outside inferred source coverage.
Trend Theater
Four anchor years show how the burden story changed from 1990 to 2021 under the selected disease lens.
IHME Surface Map
Score Explainer
Think of this like a confidence meter. A higher score means the country appears to have a stronger and more visible evidence picture in this prototype.
So if we can see more of the population, have more source material, and the estimates are less uncertain, the coverage score goes up.
Think of this like a worry meter. A higher score means a country may be both important and under-observed at the same time.
If a country has high burden, many people outside likely coverage, high uncertainty, old inputs, and only a few sources, the blind-spot score goes up.
If the disease burden looks bigger than in many other countries, that adds to concern.
If a lot of people appear to be outside reported coverage, that adds to concern.
If the estimate is fuzzy, wide, or unstable, that adds to concern.
If the latest source is old, the picture may be outdated.
If there are only a few sources, the evidence picture may be thin.
Imagine two countries. Country A has a lot of disease, many people outside coverage, and only a few old sources. Country B has less disease and many recent sources. Country A gets a higher blind-spot score because it looks more important and less visible at the same time.
Method
Prototype demo formula: gap score = 0.35 × burden percentile + 0.25 × uncovered population share + 0.20 × uncertainty + 0.10 × source staleness + 0.10 × source sparsity
In production, burden values should come directly from IHME GBD exports, while evidence context should be refreshed from the GBD Sources Tool, Epi Visualization, mortality tools, and country profile context. This prototype deliberately separates direct indicators from inferred scores so the simplifications remain visible.
The intended official inputs for this atlas are IHME burden estimates, IHME source
metadata, and IHME uncertainty outputs. By contrast, the coverage score,
blind-spot score, and uncovered population share shown in this
prototype are inferred constructs created for this website.
The weights 0.35 / 0.25 / 0.20 / 0.10 / 0.10 were judgment-based design
choices. They were chosen to emphasize disease burden first, then population potentially
outside observed coverage, then uncertainty, with smaller penalties for older and sparser
source environments.
These weights were not copied from IHME and do not represent an endorsed methodology from IHME, WHO, OECD, or any other institution. They were informed by general guidance on composite indicators and health data quality, especially the principle that weighting should follow an explicit conceptual framework and that weights often reflect analytic judgment rather than objective truth.
These simplifications are acceptable for an exploratory dashboard because the aim is to help users detect patterns, compare places, and identify where deeper evidence review may be warranted. They are not a substitute for formal methodological reporting, and this site should always label the evidence-gap layer as experimental, inferred, or prototype.
The choice of weights in this prototype is an inference informed by these general methodological references. It is not a published rule from any of the sources above.