论文标题
病原体：朱莉娅的传染病传播网络建模
Pathogen.jl: Infectious Disease Transmission Network Modelling with Julia
论文作者
论文摘要
我们介绍了病原体，以模拟和推断传播网络模型（TN-ILM）的传染病传染性疾病在连续时间扩散。 TN-ILM可用于通过Markov Chain Monte Carlo（MCMC）在贝叶斯框架内共同推断传输网络，事件时间和模型参数。我们详细介绍了为TN-ILMS执行MCMC的具体策略，并在Julia软件包（Pathenentogenty.jl）中实施了这些策略，该策略利用了Julia语言的关键特征。我们提供了一个使用病原体的示例，以模拟易感性诱发（SIR）TN-ILM之后模拟流行病，然后使用该流行病产生的观测值进行推理。我们还通过将TN-ILMS应用于1861年在德国Hagelloch发生的麻疹暴发的数据中的数据来证明病原体。
We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992).
