A public health decision support system framework for the evaluation of infectious disease mitigation

从1347年至1350年的黑死病和1918年至1919年的西班牙流感大流行，到最近的2003年SARS疫情和2009年流感大流行，以及无数儿童感染的爆发，传染病在整个有记录的历史中一直是人类的祸害，导致重大的发病率、死亡率和社会经济动荡。鉴于历史证据和最近关于传染病暴发的经验，公共卫生官员显然需要迅速和有力的决策，以制定有效的战略，以减轻未来传染病暴发的影响。传染病模型在为公共卫生专家提供决策支持方面发挥了重要作用，特别是在SARS以来。 我们建议设计和开发一个全面的软件框架来评估传染病缓解策略。具体地说，对于特定的未来传染病暴发，这一框架将用于设计、开发和快速部署决策支持系统，公共卫生专家将使用该系统来确定该暴发的有效缓解战略。针对暴发的决策支持系统必须满足三个标准：可靠性(在初始条件下生成结果的有效性)、计算效率以及对实际考虑和基本知识变化的适应性。 我们这项提议的目的是通过一种强大的传染病建模方法来创造新的基础知识。这种方法将使假设变得明确，提高对疾病机制的理解，并提供可靠的预测。最重要的是，通过与公共卫生专家的密切互动，我们还将突出关键因素，以确定公共卫生政策要求，更有效地应用控制措施，并改善加拿大的医疗保健结果。

From the Black Death of 1347-1350 and the Spanish influenza pandemic of 1918-1919, to the more recent 2003 SARS outbreak and the 2009 influenza pandemic, as well as countless outbreaks of childhood infections, infectious diseases have been the bane of humanity throughout recorded history causing significant morbidity, mortality, and socioeconomic upheaval. Given the historical evidence and the recent experiences with infectious disease outbreaks, it is clear that public health officials need rapid and robust decision-making to produce effective strategies to mitigate the effects of future infectious disease outbreaks. Infectious disease modeling, especially since SARS, has played an important role in providing decision support to public health experts. We propose to design and develop a comprehensive software framework to evaluate infectious disease mitigation strategies. Specifically, for a given future infectious disease outbreak, this framework would be used to design, develop and rapidly deploy a decision support system that would be used by public health experts to identify effective mitigation strategies for that outbreak. An outbreak-specific decision support system must satisfy three criteria: reliability (validity of generated results with initial conditions), computational efficiency, and adaptability to practical considerations and changes to fundamental knowledge. Our intention with this proposal is to create new fundamental knowledge via a robust infectious disease modeling methodology. This methodology will make assumptions explicit, improve understanding of disease mechanisms, and provide sound predictions. Most importantly, via close interactions with public health experts, we will also highlight key factors to determine public health policy requirements, apply control measures more effectively, and improve Canadian healthcare outcomes.