Researchers Map State Socioeconomic Vulnerability To ILI
Tripathy et al. (published January 28, 2026) develop a machine-learning framework to map state-level socio-economic vulnerability to Influenza-like Illness in the United States for 2022, integrating 39 census-derived indicators and applying Random Forest regression. They identify migration, insurance coverage, and proportions of female and elderly populations as key drivers, finding DC, Massachusetts, Hawaii, New Mexico, and Rhode Island most vulnerable (indices >0.35).
Scoring Rationale
Strong ML methodology and peer-reviewed credibility, with practical maps for intervention; limited by US-state scope and disease-specific focus.
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