Outbreak Simulation and Planning

爆发模拟和规划

• 批准号: 7487794
• 负责人: Wayne Loschen
• 金额: $ 36.61万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7487794
• 关键词: Outbreak; Simulation; Planning

Syndromic surveillance systems are becoming quite common in public health departments around the country. These systems allow epidemiologists and other users to monitor disease trends in their communities, generally with the primary objective of identifying and acting on unusual disease patterns, whether natural or man-made, as quickly as possible. Most of the active surveillance systems are used daily by the responsible public health personnel who rapidly become adept at identifying abnormal patterns of disease in their communities and quickly come to recognize the patterns that describe normal seasonal variations in disease. They have little opportunity, however, to see how their normal daily view would change during a disease outbreak caused by a terrorist event because, luckily, few have occurred since most systems were installed. The only way users will become familiar with how their systems react during a man-made outbreak is to participate in training exercises in which simulated data injected into their system mimics outbreak conditions. Unfortunately the creation of such exercises for these systems is a complex task which is beyond the scope of most health departments. The purpose of this project is to produce aframework of standards and software tools, the Exercise/Simulation Framework (ESF), that can be used with multiple syndromic surveillance systems to create 'table top' exercises that mimic disease outbreaks. These exercises can be used for training purposes, to help model the effect of public health response protocols such as mass prophylaxis in a specific community, and to help develop public health response plans for use in emergency situations. In addition the ESF can be used to evaluate the effect of surveillance algorithms under a variety of different disease patterns.

综合征监测系统在公共卫生部门越来越普遍 这个国家。这些系统使流行病学家和其他用户可以监测其疾病趋势 社区通常以识别和对异常疾病模式的主要目标， 无论是自然还是人造的，尽可能快。大多数主动监视系统都使用 每天由负责任的公共卫生人员迅速熟练识别异常 社区中疾病的模式，并迅速认识到描述正常的模式 疾病的季节性变化。但是，他们几乎没有机会看看他们的日常观点如何 在恐怖事件引起的疾病爆发期间会发生变化，因为幸运的是，很少发生 由于大多数系统已安装。用户熟悉其系统反应的唯一方法 在人造爆发期间，将参加训练练习，其中模拟数据注入 他们的系统模仿爆发条件。不幸的是，为这些系统创建了此类练习 是一项复杂的任务，超出了大多数卫生部门的范围。 该项目的目的是生产标准和软件工具的Aframework， 锻炼/仿真框架（ESF），可以与多个综合征监视系统一起使用 创建模仿疾病爆发的“桌面”练习。这些练习可用于培训 目的，帮助建模公共卫生反应方案的影响，例如大规模预防 特定的社区，并帮助制定公共卫生响应计划以在紧急情况下使用。在 补充ESF可用于评估监视算法在各种不同的不同 疾病模式。