Pathogen.jl: Infectious Disease Transmission Network Modelling with Julia

具有离散和多次暴露的新治愈率模型

A New Cure Rate Model with Discrete and Multiple Exposures

针对传染病多次暴露场景，提出能处理过离散与欠离散的新治愈率模型，基于EM算法进行参数估计，并通过模拟和COVID-19数据验证了模型有效性与灵活性。

sourceR: Classification and Source Attribution of Infectious Agents among Heterogeneous Populations

Zoonotic diseases are a major cause of morbidity, and productivity losses in both humans and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human…

分子传染病流行病学：生存分析与将系统发育树链接到传播树的算法

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

本文提出在生存分析框架下开发算法，利用系统发育树与流行病学数据的一一对应关系枚举传播树，从而更高效地估计传染性风险比和基线风险，为分子传染病流行病学提供严谨灵活的统计基础。

Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely-used modeling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far over-estimated, leading to the potential for lar…

Inferring the sources of HIV infection in Africa from deep sequence data with semi-parametric Bayesian Poisson flow models

Pathogen deep-sequencing is an increasingly routinely used technology in infectious disease surveillance. We present a semi-parametric Bayesian Poisson model to exploit these emerging data for inferring infectious disease transmission flows and the sources of infection at the population level. The framework is computationally scalable in high dimensional flow spaces thanks to Hilbert Space Gaussian process approximations, allows for sampling bias adjustments, and estimation of gender- and age-specific transmission flows at finer resolution than previously possible. We apply the approach to den…

Randomization for the susceptibility effect of an infectious disease intervention

Randomized trials of infectious disease interventions, such as vaccines, often focus on groups of connected or potentially interacting individuals. When the pathogen of interest is transmissible between study subjects, interference may occur: individual infection outcomes may depend on treatments received by others. Epidemiologists have defined the primary causal effect of interest -- called the "susceptibility effect" -- as a contrast in infection risk under treatment versus no treatment, while holding exposure to infectiousness constant. A related quantity -- the "direct effect" -- is define…