BioSense Initiative to Improve Early Outbreak Detection

BioSense 计划改善早期疫情检测

• 批准号: 7098604
• 负责人: HOWARD S. BURKOM
• 金额: $ 60.12万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2008-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7098604
• 关键词: biohazard detection; decision making; disease outbreaks; human data; information systems; method development; public health; statistics /biometry

The broad project objective is to establish a robust technical link in the process of automated outbreak detection to complement and backup the traditional sentinel surveillance system. Means to this objective include development and efficient combination of data-driven statistical alerting algorithms and implementation of higher level decision-support tools for fusing algorithmic results with external information and epidemiologist judgment. Specific aims are to establish and exercise a context-sensitive testbed for standardized algorithm development and evaluation, to develop an information-sharing methodology for jurisdictional situations that preclude data-sharing, and to create decision-support tools by combining heuristic methods used by experience health monitors with Bayesian Belief Net representation. Research within the testbed will advance the state of the art in detection algorithms, stressing the adaptations required to make them relevant and effective for monitoring on a daily or, depending on input data rates, a near-realtime basis. Algorithms will broadly include univariate, multivariate hypothesis tests and data mining techniques more general automated learning and we will seek to determine the appropriate niche for each approach found useful. For univariate data, hypothesis test research will investigate means of combining data modeling and process control for optimal detection performance depending on the data background. Multivariate algorithm research will include both fully multivariate methods and multiple univariate methods and will blend the two for optimal monitoring capability. Structured testbed design and development will establish standards for algorithm evaluation and comparison based on health monitoring effectiveness, measured by sensitivity, specificity, and timeliness of event detection. These standards will make this public health research area more accessible and relevant to local heath department users. Potentially, the decision-support tools may make complex data scenarios understandable to users of these systems. Acceptance of potential of automated surveillance systems will stimulate increased cooperation of health departments, data providers, and policymakers, further improving public health monitoring capability.

广泛的项目目标是在自动爆发过程中建立强大的技术链接 检测以补充和备份传统的前哨监视系统。意味着这个目标 包括开发和有效组合数据驱动的统计警报算法和 实施更高级别的决策支持工具，用于将算法结果与外部信息融合 和流行病学家的判断。具体目的是建立和行使上下文敏感的测试床 标准化算法开发和评估，以开发一种信息共享方法 管辖权的情况排除数据共享，并通过结合来创建决策支持工具 经验有贝叶斯信念净表示的健康监视器使用的启发式方法。研究 在测试床内将在检测算法中推动最新技术，并强调所需的适应 为了使它们相关且有效地监视每日或根据输入数据速率，接近时间 基础。算法将广泛包括单变量，多元假设测试和数据挖掘 技术更一般的自动化学习，我们将寻求确定每个人的适当利基 方法发现有用。对于单变量数据，假设检验研究将研究合并的手段 数据建模和过程控制，以取决于数据背景的最佳检测性能。 多元算法研究将包括完全多元方法和多种单变量方法 并将两者融合以获得最佳监视能力。结构化的测试床设计和开发将 建立基于健康监测有效性的算法评估和比较标准， 通过灵敏度，特异性和事件检测的及时性来衡量。这些标准将公开 卫生研究领域更容易访问，并且与当地的荒地部门用户相关。可能， 决策支持工具可能使这些系统用户可以理解复杂的数据方案。 接受自动监视系统的潜力将刺激健康的增加 部门，数据提供商和政策制定者，进一步提高了公共卫生监测能力。