Disaster preparedness policies depend on vulnerability indices to guide disaster management. Yet, commonly used tools such as the CDC Social Vulnerability Index and FEMA’s National Risk Index rely largely on static demographic proxies, producing broad vulnerability scores that offer limited insight into the specific needs communities will face during disasters. These approaches can obscure how hazards translate into different forms of harm, such as medical needs during power outages or housing repair needs after storms, and risk embedding structural bias into preparedness decisions. This research shows that vulnerability can be reframed as directly predicting disaster-related needs. We develop the Local Impact Vulnerability Assessment (LIVA), a framework that integrates demographic, housing, socioeconomic, and health indicators with historical disaster outcomes to estimate hazard-specific community needs. Using data from federally declared disasters between 2018 and 2025, LIVA demonstrates substantially stronger alignment with observed FEMA assistance needs across hazards and need categories than existing vulnerability and risk indices. The findings suggest that disaster preparedness policy should advance beyond static indices to tailored predictive planning tools that anticipate how different hazards generate different community needs. By identifying where specific forms of assistance are likely to be required, emergency management organizations can plan, prepare, and improve the equity and effectiveness of disaster response.
Roderick et al. (2026) studied this question.