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

尽管加拿大过去取得了巨大成就，但仍面临着对公共卫生的紧迫威胁。虽然制定适当的公共卫生政策需要明智地选择干预策略，但由于干预措施及其效果之间的复杂关系，这种选择变得复杂--增加了即使是善意的政策也可能被证明无效或造成的问题比它们解决的更严重的风险。 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模拟模型(ABM)来获得洞察力。通过让决策者在“虚拟人口”中模拟政策的影响，这种模型可以帮助设计高杠杆、强大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顺序，并帮助与不同利益攸关方进行沟通。不幸的是，这样的模型常常是不必要的挑战，难以构建、难以理解、计算成本高昂，并且难以在团队环境中共享和协作探索。通常，也没有足够的关于个人行为的观察数据，无法确信虚拟人口与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，以了解人口健康趋势和卫生政策权衡。具体地说，我们寻求使这类模型更易于使用和更快地模拟，从而为仔细调查政策影响提供更多时间。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，并支持更清晰的模型描述方法，从而帮助构建设计更好、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，为模型代表人们的行为提供信息，并允许将模型结果与观测数据进行交叉验证。最后，为了帮助提高ABM的准确性，我们将提供自动校正不准确模型的方法。

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.