Optimising disease surveillance to support decision-making

优化疾病监测以支持决策

• 批准号: DP240102286
• 负责人: Dr Freya Shearer
• 金额: $ 29.31万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240102286
• 关键词: Optimising; disease; surveillance; support; decision

COVID-19 has demonstrated the critical role of epidemic data and analytics in guiding government response to pandemic threats, reducing disease and saving lives. The demand for epidemic analytics for response to threats of national significance will only grow. The goals of this project are to 1) determine the combination(s) of surveillance methods that provide the most useful data for epidemic analysis and 2) translate these findings into the blueprint for a next-generation infectious disease surveillance system for Australia. We will use a simulation-evaluation approach, coupling methods from infectious disease modelling with those from information theory optimal design. Outcomes will enable more tailored and effective pandemic response.

2019冠状病毒病疫情证明了疫情数据和分析在指导政府应对疫情威胁、减少疾病和拯救生命方面的关键作用。为应对国家重大威胁而进行流行病分析的需求只会增加。该项目的目标是：1）确定为流行病分析提供最有用数据的监测方法组合; 2）将这些发现转化为澳大利亚下一代传染病监测系统的蓝图。我们将使用模拟评估方法，从传染病建模与信息论优化设计的耦合方法。这些成果将有助于更有针对性和更有效地应对大流行病。