应用SMC $^2$对产超广谱β-内酰胺酶大肠埃希菌和肺炎克雷伯菌数据的推断
Inference on Extended-Spectrum Beta-Lactamase Escherichia coli and Klebsiella pneumoniae data through SMC$^2$
摘要：
我们提出了一种新的随机模型的抗菌素耐药菌在人口中的传播，以及一个有效的算法，这样的模型拟合样本数据。我们引入了一个基于个体的流行病模型，模型的状态决定了哪些个体被细菌定殖。流行病的传播率考虑到了个人的位置、个人协变量、季节性和环境影响。我们模型的状态只是部分观察到的，数据由来自家庭样本的个人的测试结果组成，大约每周两次，持续19个月。由于我们的模型的状态空间很大，因此将我们的模型拟合到数据是一个挑战。我们开发了一个有效的SMC $^2$算法来估计参数和比较模型的传输速率。我们通过使用底层流行病模型的尺度不变性，以计算效率高的方式实现了该算法，这意味着我们可以为数万人而不是数百万人的群体定义和拟合我们的模型。我们的激励应用程序的重点是社区获得的产超广谱β-内酰胺酶大肠杆菌（E。coli）和肺炎克雷伯氏菌（Klebsiella pneumoniae，K. pneumoniae），使用作为乌干达和马拉维耐药驱动因素项目的一部分收集的数据。我们推断模型的参数，并学习关键的流行病数量，如有效繁殖数，流行的空间分布，家庭集群动态和季节性。
英文摘要：
We propose a novel stochastic model for the spread of antimicrobial-resistant bacteria in a population, together with an efficient algorithm for fitting such a model to sample data. We introduce an individual-based model for the epidemic, with the state of the model determining which individuals are colonised by the bacteria. The transmission rate of the epidemic takes into account both individuals' locations, individuals covariates, seasonality and environmental effects. The state of our model is only partially observed, with data consisting of test results from individuals from a sample of households taken roughly twice a week for 19 months. Fitting our model to data is challenging due to the large state space of our model. We develop an efficient SMC$^2$ algorithm to estimate parameters and compare models for the transmission rate. We implement this algorithm in a computationally efficient manner by using the scale invariance properties of the underlying epidemic model, which means we can define and fit our model for a population on the order of tens of thousands of individuals rather than millions. Our motivating application focuses on the dynamics of community-acquired Extended-Spectrum Beta-Lactamase-producing Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae), using data collected as part of the Drivers of Resistance in Uganda and Malawi project. We infer the parameters of the model and learn key epidemic quantities such as the effective reproduction number, spatial distribution of prevalence, household cluster dynamics, and seasonality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
