Stocking Hygeia's toolbox: methodological innovation in support of computational epidemiology

储备 Hygeia 的工具箱：支持计算流行病学的方法创新

• 批准号: 327290-2011
• 负责人: Osgood, Nathaniel
• 金额: $ 1.02万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571972
• 关键词: Stocking; Hygeia; toolbox; methodological; innovation

Despite great past achievements, Canada faces pressing threats to public health. While designing appropriate public health policies requires judicious choice of intervention strategies, such choice is complicated by the complex relationships between interventions and their effects -- raising the risk that even well-intended policies may prove ineffective or cause worse problems than they solve. To enable them to better grapple with these challenges, public health decision makers increasingly turn to agent-based simulation models (ABMs) for insight. By letting decision makers simulate the effects of policies in "virtual populations", such models can help inform the design of health interventions that are high leverage, robust, and cost-effective, facilitate quicker reaction to an infectious disease outbreak, aid in understanding of health trends, help prioritize data collection, and assist in communication with diverse stakeholders. Unfortunately, such models are frequently needlessly challenging to build, hard to understand, computationally expensive, and are difficult to share and collaboratively explore in a team environment. Frequently there is also insufficient observed data on individual behavior to be confident about the fidelity of the virtual populations to the real-world population. Within our proposed work, we seek to facilitate the design and construction of ABMs that inform understanding of population health trends and health policy tradeoffs. Specifically, we seek to make such models easier to use and faster to simulate, thereby opening up more time to carefully investigate policy impacts. We will also aid the construction of models that are better designed and more widely shared and understood by building software to improve team access to models and model results, and by supporting clearer ways of describing models. We will provide smartphone applications to collect rich data to inform models' representation of peoples' behaviour, and to permit cross-checking model results against observed data. Finally, to help improve the ABM accuracy, we will provide ways to automatically correct inaccurate models.

尽管过去取得了巨大成就，但加拿大面临着对公共卫生的紧迫威胁。 虽然设计适当的公共卫生政策需要明智地选择干预战略，但这种选择因干预措施与其效果之间的复杂关系而变得复杂-增加了这样的风险，即即使意图良好的政策也可能证明无效或造成比解决的问题更严重的问题。 为了使他们能够更好地应对这些挑战，公共卫生决策者越来越多地转向基于代理的仿真模型（ABM）的洞察力。 通过让决策者模拟政策在“虚拟人群”中的影响，这些模型可以帮助设计高杠杆、强有力和具有成本效益的卫生干预措施，促进对传染病爆发的更快反应，帮助了解卫生趋势，帮助确定数据收集的优先次序，并帮助与不同的利益攸关方进行沟通。 不幸的是，这样的模型往往是不必要的挑战，难以理解，计算昂贵，很难在团队环境中共享和协作探索。 通常也没有足够的个人行为的观察数据来确信虚拟人口对现实世界人口的保真度。 在我们拟议的工作中，我们寻求促进ABM的设计和建设，为了解人口健康趋势和卫生政策权衡提供信息。 具体而言，我们力求使这些模型更容易使用，更快地模拟，从而腾出更多的时间来仔细研究政策影响。我们还将通过构建软件来改善团队对模型和模型结果的访问，并通过支持更清晰的描述模型的方式，来帮助构建设计更好、更广泛共享和理解的模型。 我们将提供智能手机应用程序来收集丰富的数据，以告知模型对人们行为的表示，并允许根据观察到的数据交叉检查模型结果。 最后，为了帮助提高ABM的准确性，我们将提供自动纠正不准确模型的方法。