EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology

Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/www/emulsion-public.随机机理流行病学模型（stochastic mechanistic epidemiological models）对于深入理解病原体的出现与传播、评估不同尺度（从宿主内到跨国尺度）的防控策略具有重要价值。然而，在预测流行病学领域中，开发需要整合多学科知识的实用模型面临三大核心挑战：转化为仿真代码后可读性差、可复现性与可复用性较低，且相较于疫情暴发的时间尺度，模型开发周期过长。本文介绍了EMULSION：一款基于人工智能（Artificial Intelligence）的软件，旨在解决上述问题，帮助建模人员将精力集中于模型设计而非编程工作。EMULSION 定义了一种领域特定语言（domain-specific language），可将流行病学模型的所有组成部分（结构、过程、参数等）以结构化文本文件的形式清晰呈现。该文件可被其他领域的科研人员（流行病学家、生物学家、经济学家等）读懂，他们能够在模型开发的任一阶段参与验证或修正模型假设。随后EMULSION的通用仿真引擎会自动处理该文件，避免模型描述与实际实现之间出现不一致。该建模语言与仿真架构均依托先进人工智能方法（知识表示与多级基于智能体的仿真）的结合，可支持多种建模范式（从分室模型（compartment model）到个体基模型（individual-based model））在不同尺度（最高可达复合种群（metapopulation）尺度）下的构建。本文通过一系列复杂度逐步提升的示例（包括对模型核心假设的后期修订），展示了EMULSION的灵活性与对迭代式建模的支持能力。目前EMULSION已被应用于真实病害系统（pathosystem）中多种疾病的传播建模工作。EMULSION 提供了命令行工具，可用于模型检查、生成模型图示、运行仿真以及绘制仿真结果图。EMULSION 采用Python 3编写，可在Linux、MacOS及Windows操作系统上运行。该软件采用Apache-2.0开源许可证发布。相关安装指南、教程与大量示例的完整文档可通过以下链接获取：https://sourcesup.renater.fr/www/emulsion-public。