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Supplement for Cloud Computing: Opioid Policy Models

云计算的补充：阿片类药物政策模型

基本信息

批准号：
10826888
负责人：
GEORGIY BOBASHEV
金额：
$ 23.64万
依托单位：
RESEARCH TRIANGLE INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10826888
关键词：
Address Administrative Supplement Affect Benchmarking Buprenorphine Calibration Caring Cessation of life Cloud Computing Collaborations Communities Complex Computer software Consumption County Data Science Development Disease Environment Experimental Designs Funding Future Geographic Locations Geography Goals Grant Guidelines Harm Reduction Health Personnel Health Professional Health Services Accessibility Hospitals Individual Insurance Coverage Intervention Law Enforcement Location Modeling Nature North Carolina Opioid Outcome Overdose Paper Pathway interactions Patients Performance Pharmaceutical Preparations Pharmacy facility Physicians Policies Policy Maker Population Positioning Attribute Prevention Price Public Health Public Policy Pythons Research Research Personnel Research Project Grants Research Support Resources Running Rural Rural Appalachia Services Side Speed Testing Time Travel Uncertainty United States United States Dept. of Health and Human Services United States National Institutes of Health Validation access disparities advanced analytics analytical method cloud based computer infrastructure cost cost effective intervention cost effectiveness design effectiveness evaluation experimental study improved interest intervention effect model design models and simulation multiple datasets opioid misuse opioid overdose opioid policy overdose death overdose risk parent grant racial disparity scale up service providers simulation statistics tool urban area

项目摘要

PROJECT SUMMARY/ABSTRACT In our study, Opioid Policy Modeling in North Carolina (5R01DA04799), we developed an agent-based simulation model (ABM) to inform policy makers and health professionals in North Carolina about local and state-wide interventions to reduce opioid overdoses and related deaths. Interventions are identified in the NC Opioid Action Plan and cover the Three Pillars: prevention, connection to care, and harm reduction. Our ABM represents a community (e.g., a county, town) of networked individuals (e.g., patients, physicians, dealers), and simulates how proposed interventions affect individual pathways to opioid misuse and other outcomes (i.e., OD death). The model relies on a synthetic population representing every individual in the United States, which allows information from multiple datasets to be probabilistically connected in one model. Synthetic individuals are positioned in geospatial context that includes the locations of hospitals, other treatment facilities, and service providers. Cloud infrastructure will allow us to speed up realistic simulations and consider more scenario options than are considered on a current “in-house” computational infrastructure. It will enable us to develop a meta- model summarizing a multitude of scenarios. Benefits of cloud computing will affect geospatial calibration, validation, and scenario simulations.

项目总结/摘要在我们的研究中，北卡罗来纳州的阿片类药物政策建模（5 R 01 DA 04799），我们开发了一个基于代理的模拟模型（ABM），以告知北卡罗来纳州的政策制定者和卫生专业人员，全州范围内的干预措施，以减少阿片类药物过量和相关死亡。在国家信息通报中确定了干预措施阿片类药物行动计划涵盖三大支柱：预防，与护理的联系和减少伤害。我们的ABM代表一个社区（例如，县、镇）的联网个人（例如，病人，医生，经销商），并模拟拟议的干预措施如何影响阿片类药物滥用和其他结果（即，OD死亡）。该模型依赖于一个合成的人口代表每一个人在美国，它允许多个数据集的信息在一个数据集中概率连接模型合成个体被定位在地理空间背景中，包括医院的位置，其他治疗设施和服务提供商。云基础设施将使我们能够加快现实的模拟，并考虑更多的场景选项，在当前的“内部”计算基础设施上进行考虑。它将使我们能够开发一个Meta- 模型总结了多种情况。云计算的好处将影响地理空间校准，验证和情景模拟。