1. The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.
2. We here introduce nosoi, an open‐source r package that offers a complete, tunable and expandable agent‐based framework to simulate transmission chains under a wide range of epidemiological scenarios for single‐host and dual‐host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.
3. Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user‐specified rules or data, such as travel patterns between locations.
4. nosoi is able to generate a multitude of epidemic scenarios, that can—for example—be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands‐on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

中文翻译：

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nosoi：r中基于随机代理的传输链仿真框架

1. 传染媒介的传播过程创建了由传播事件链接的主机的连接链，称为传播链。重建传播链仍然是一项具有挑战性的工作，但在以监视和流行病学调查为特征的罕见情况下除外。推理框架试图估计或近似这些传输链，但是这种方法的准确性和有效性通常缺乏对观察到实际传输链的数据集的正式评估。
2. 我们在这里介绍nosoi，这是一个开源r包，它提供了一个完整的，可调整的和可扩展的，基于代理的框架，可以在多种流行病学情况下针对单宿主和双宿主流行情况模拟传播链。可通过GitHub和CRAN访问nosoi，并附带大量文档，提供帮助和实践示例，以帮助用户设置自己的仿真。
3. 一旦被感染，每个主机或代理在每个时间步中都会经历一系列事件，例如在（位置之间）移动或传播感染，所有这些都是由用户指定的规则或数据（例如位置之间的出行方式）驱动的。
4. nosoi能够产生多种流行病，例如，可以用来验证各种重建方法，包括流行病建模和系统动力学分析。nosoi还提供了一个全面的框架来利用根据经验获取的数据，使用户能够探索参数的变化如何影响流行病的可能性。除了研究问题外，nosoi还可以为讲师提供完整的教学工具，为学生提供对流行病学过程动态及其影响因素的动手探索。由于该软件包不依赖数学形式主义，而是使用更直观的算法方法，因此甚至可以轻松快速地实现整个模型的广泛更改。