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Moving interdisciplinary science forward: integrating participatory modelling with mathematical modelling of zoonotic disease in Africa

机译：推动跨学科科学的发展：将参与式建模与非洲人畜共患病的数学建模相结合

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This review outlines the benefits of using multiple approaches to improve model design and facilitate multidisciplinary research into infectious diseases, as well as showing and proposing practical examples of effective integration. It looks particularly at the benefits of using participatory research in conjunction with traditional modelling methods to potentially improve disease research, control and management. Integrated approaches can lead to more realistic mathematical models which in turn can assist with making policy decisions that reduce disease and benefit local people. The emergence, risk, spread and control of diseases are affected by many complex bio-physical, environmental and socio-economic factors. These include climate and environmental change, land-use variation, changes in population and people’s behaviour. The evidence base for this scoping review comes from the work of a consortium, with the aim of integrating modelling approaches traditionally used in epidemiological, ecological and development research. A total of five examples of the impacts of participatory research on the choice of model structure are presented. Example 1 focused on using participatory research as a tool to structure a model. Example 2 looks at identifying the most relevant parameters of the system. Example 3 concentrates on identifying the most relevant regime of the system (e.g., temporal stability or otherwise), Example 4 examines the feedbacks from mathematical models to guide participatory research and Example 5 goes beyond the so-far described two-way interplay between participatory and mathematical approaches to look at the integration of multiple methods and frameworks. This scoping review describes examples of best practice in the use of participatory methods, illustrating their potential to overcome disciplinary hurdles and promote multidisciplinary collaboration, with the aim of making models and their predictions more useful for decision-making and policy formulation.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0110-4) contains supplementary material, which is available to authorized users.
机译：这篇综述概述了使用多种方法来改进模型设计并促进对传染病的多学科研究的好处，并展示并提出了有效整合的实际例子。它特别探讨了将参与式研究与传统建模方法结合使用以潜在地改善疾病研究，控制和管理的益处。综合方法可以产生更现实的数学模型，进而可以帮助制定减少疾病，造福当地人民的政策。疾病的出现，风险，传播和控制受到许多复杂的生物物理，环境和社会经济因素的影响。其中包括气候和环境变化，土地利用变化，人口和人们的行为变化。此范围界定审查的证据基础来自一个财团的工作，旨在整合流行病学，生态学和发展研究中传统使用的建模方法。共有五个实例说明了参与性研究对模型结构选择的影响。示例1集中于使用参与式研究作为构建模型的工具。例2着眼于识别系统中最相关的参数。例3着重于确定系统的最相关机制（例如时间稳定性或其他方面），例4检验了数学模型的反馈以指导参与性研究，而例5超出了迄今为止所描述的参与性和参与性之间的双向相互作用。多种方法和框架集成的数学方法。这份范围概述性评论描述了使用参与式方法的最佳实践示例，说明了其克服学科障碍和促进多学科合作的潜力，旨在使模型及其预测对决策和政策制定更加有用。本文的版本（doi：10.1186 / s40249-016-0110-4）包含补充材料，授权用户可以使用。

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