Preprints
https://doi.org/10.5194/nhess-2020-402
https://doi.org/10.5194/nhess-2020-402

  04 Feb 2021

04 Feb 2021

Review status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

A simulation-optimization framework for post-disaster allocation of mental health resources

Stephen Cunningham, Steven Schuldt, Christopher Chini, and Justin Delorit Stephen Cunningham et al.
  • Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA

Abstract. Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomes varies based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental health outcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers and psychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligations that constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition and linear optimization model that assists planners in optimally allocating constrained resources and satisfying mental health recovery priorities post-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's (CDC) Social Vulnerability Index (SVI) to identify vulnerable populations needing additional assistance post-disaster. In this study, we optimally distribute mental health clinicians to treat the affected population based upon rulesets that simulate decision-maker priorities, such as economic and social vulnerability criteria. Finally, the resource assignment framework maps the mental health recovery of the disaster-affected populations over time, providing agencies a means to prepare for and respond to future disasters given existing resource constraints. These capabilities hold the potential to support decision-makers in minimizing long-term mental health impacts of disasters on communities through improved preparation and response activities.

Stephen Cunningham et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2020-402', Anonymous Referee #1, 31 May 2021
    • AC1: 'Reply on RC1', Stephen Cunningham, 15 Jul 2021
  • RC2: 'Comment on nhess-2020-402', Anonymous Referee #2, 05 Jun 2021
    • AC2: 'Reply on RC2', Stephen Cunningham, 15 Jul 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2020-402', Anonymous Referee #1, 31 May 2021
    • AC1: 'Reply on RC1', Stephen Cunningham, 15 Jul 2021
  • RC2: 'Comment on nhess-2020-402', Anonymous Referee #2, 05 Jun 2021
    • AC2: 'Reply on RC2', Stephen Cunningham, 15 Jul 2021

Stephen Cunningham et al.

Stephen Cunningham et al.

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Short summary
The severity of disaster-induced mental health illness outcomes varies based on factors such as socioeconomic standing, age, and degree of exposure. This research proposes a resource allocation framework allowing decision-makers the capability to assess the capacity and scalability of early, intermediate, and long-term mental health treatment and recovery. Ultimately, this framework can inform policy and operational decisions based on community needs and constrained resources post-disaster.
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