NIRG: Rule-based epidemic models

病原体在人与人之间传播的流行病模型自然与导致传播的个体之间的相互作用有关。这显然是一个主要的简化；有许多过程在起作用，从流行病对行为和干预措施的反馈循环，到限制生产预防措施和提供诊断测试的资源限制，再到免疫系统对病原体和药物的反应。流行病模型通常不包括对这些极具影响力的过程的解释。相反，有时只包括这些过程的假定效果。这极大地限制了流行病模型的范围。相比之下，在分子生物学中，通常要考虑更大种类的可能的相互作用。对于具有多交互作用的系统，既有表达和模拟的方法，也有成熟的软件。我们已经成功地证明，这些技术可以有效地直接应用于流行病，包括在包含免疫反应的多尺度环境中，以及在适当的扩展下，在复杂的社区环境中详细地重建流行病。我们将以这一成功为基础，巩固传染病建模界的这一能力。我们将改善我们在开拓性工作中使用的工具的可及性，促进更广泛地采用我们的流行病建模方法。我们将开展一系列案例研究，建立一个实践共同体，建立有文件记录和标准化的方法，使我们的先进技术能够解决当前和未来与减少传染病造成的公共卫生负担有关的紧迫问题。

Epidemic models for pathogens transmitted from human to human are, naturally, concerned with the interaction between individuals that leads to transmission. This is clearly a major simplification; there are many processes at work, from the feedack loop of epidemics on behaviour and interventions, to resource constraints limiting the production of prophylaxis and availability of diagnostic tests, to the response of the immune system to the pathogen and pharmaceuticals. Epidemic models do not normally include an account of these highly influential processes. Instead, only the assumed effect of these processes is sometimes included. This strongly limits the scope of epidemic models.By contrast, in molecular biology, it is typical to consider a much larger class of possible interactions. There exist methods as well as mature software for expressing and simulating systems with many interactions. We have successfully shown that these techniques can be fruitfully applied directly to epidemics, including in a multi- scale setting incorporating immune response and, with suitable extensions, to detailed epidemic reconstruction in a complex community setting.We will build on this success in order to consolidate this capability within the infectious disease modelling community. We will improve accessibility of the tools that we used in our pioneering work, facilitating adoption of our epidemic modelling methods more widely. We will foster a community of practice by conducting a series of case studies to establish documented and standardisable approaches to bringing our advanced techniques to bear on pressing current and future questions relevant to reducing the public health burden of infectious disease.