Large-scale computational modelling of epidemics in Australia

澳大利亚流行病的大规模计算模型

• 批准号: DP160102742
• 负责人: Prof Mikhail Prokopenko
• 金额: $ 35.74万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-06-30 至 2019-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160102742
• 关键词: Large; scale; computational; modelling; epidemics

The project aims to develop novel computational epidemiological models to contribute to guidelines for optimal prophylaxis, vaccination and case management. Emerging threats posed by infectious diseases and bioterrorism could have dramatic effects on the Australian population, productivity and economy. The project aims to improve the accuracy and scope of modern computational epidemiological models by integrating large-scale Census datasets and explicitly simulating the entire population down to the level of single individuals, coupled with complex network-based and information flow analysis. The intended outcomes include a more precise and efficient forecasting of critical epidemic dynamics, and increased effectiveness of prevention, mitigation and management of socio-economic, socio-ecological and national security crises.

该项目旨在开发新的计算流行病学模型，以促进最佳预防、疫苗接种和病例管理的指导方针。传染病和生物恐怖主义构成的新威胁可能对澳大利亚的人口、生产力和经济产生巨大影响。该项目旨在通过整合大规模人口普查数据集，明确模拟整个人口到单个个体的水平，并结合复杂的基于网络和信息流的分析，提高现代计算流行病学模型的准确性和范围。预期的成果包括更精确和有效地预测关键的流行病动态，提高预防、缓解和管理社会经济、社会生态和国家安全危机的效力。