摘要：Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomesvaries based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental healthoutcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers andpsychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligationsthat constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition andlinear optimization model that assists planners in optimally allocating constrained resources and satisfying mental health recovery prioritiespost-disaster. The resource assignment framework integrates the impact of a simulated disaster on mental health, mental health provider capacities,and the Center for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) to identify vulnerable populations needing additionalassistance post-disaster. In this study, we optimally distribute mental health clinicians to treat the affected population based upon rule sets thatsimulate decision-maker priorities, such as economic and social vulnerability criteria. Finally, the resource assignment framework maps the mentalhealth recovery of the disaster-affected populations over time, providing agencies a means to prepare for and respond to future disasters givenexisting resource constraints. These capabilities hold the potential to support decision-makers in minimizing long-term mental health impacts ofdisasters on communities through improved preparation and response activities.
