SENTINEL-Dengue ASEAN-11 proposes integrating case counts, climate, satellite vegetation, population, surface water and explainable models across 11 Southeast Asian countries, with spatial and temporal validation and risk-uncertainty classes. The abstract primarily describes a proposed architecture and dataset rather than reporting predictive performance, so it should be read as a framework, not a validated operational system.
Key findings
- The designed output classifies areas as high-risk/high-confidence, high-risk/high-uncertainty, moderate-risk, low-risk or data-insufficient. However, the abstract reports no AUROC, sensitivity, lead time, calibration or country-level validation, so operational performance cannot yet be judged.
Why this matters globally
If implemented and validated, a harmonised framework that acknowledges cross-country data differences could support earlier alerts, vector-control deployment and regional coordination without concealing uncertainty.
Thai researcher contribution
J. Sudsawart of the College of Allied Health Sciences, Suan Sunandha Rajabhat University, is the Thai-affiliated author positioning Thailand within the ASEAN-11 data framework.
Limitations to consider
The abstract provides no empirical model results. Country case definitions and reporting may be non-comparable, delayed or structurally changing; Admin-1 resolution limits local action; AI cannot replace epidemiological confirmation; and data-insufficient areas may be the most vulnerable.