联合系统动力学和流行病学分析暴发特征的EpiFusion分析框架。

Q2 Pharmacology, Toxicology and Pharmaceutics
F1000Research Pub Date : 2025-03-28 eCollection Date: 2025-01-01 DOI:10.12688/f1000research.162719.1
Ciara Judge, Oliver Brady, Sarah Hill
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引用次数: 0

摘要

流行病学和病毒系统动力学领域都以疾病控制为最终目标,但各自采用的概念、方法和数据各不相同,这就赋予了它们互补的优势和不同领域的弱点。我们最近介绍了EpiFusion,这是一种通过粒子过滤使用系统发育和病例发生率数据联合推断疫情特征的模型,并演示了其用于推断模拟和真实疫情的有效繁殖数。在这里,我们提供了一系列演示使用EpiFusion分析框架进行数据分析的小插图,该框架由R包epiffusion utilities和实现模型的Java程序组成,其中包括一个使用EpiFusion上次描述以来合并的新功能的示例:提供系统发育树后向的选项,作为系统发育数据输入到程序。通过概述这些示例,我们旨在提高模型的可用性,并促进工作流的可重复性和开放研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The EpiFusion Analysis Framework for joint phylodynamic and epidemiological analysis of outbreak characteristics.

The fields of epidemiology and viral phylodynamics share the ultimate goal of disease control, but concepts, methodologies and data employed by each differ in ways that confer complementary strengths and different areas of weakness. We recently introduced EpiFusion, a model for joint inference of outbreak characteristics using phylogenetic and case incidence data via particle filtering and demonstrated its usage to infer the effective reproduction number of simulated and real outbreaks. Here we provide a series of vignettes demonstrating data analysis using the EpiFusion Analysis Framework, consisting of the R package EpiFusionUtilities and the Java program in which the model is implemented, including an example using a new feature incorporated since EpiFusion's last description: the option to provide a phylogenetic tree posterior as the phylogenetic data input to the program. By outlining these examples, we aim to improve the usability of our model, and promote workflow reproducibility and open research.

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来源期刊
F1000Research
F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)
CiteScore
5.00
自引率
0.00%
发文量
1646
审稿时长
1 weeks
期刊介绍: F1000Research publishes articles and other research outputs reporting basic scientific, scholarly, translational and clinical research across the physical and life sciences, engineering, medicine, social sciences and humanities. F1000Research is a scholarly publication platform set up for the scientific, scholarly and medical research community; each article has at least one author who is a qualified researcher, scholar or clinician actively working in their speciality and who has made a key contribution to the article. Articles must be original (not duplications). All research is suitable irrespective of the perceived level of interest or novelty; we welcome confirmatory and negative results, as well as null studies. F1000Research publishes different type of research, including clinical trials, systematic reviews, software tools, method articles, and many others. Reviews and Opinion articles providing a balanced and comprehensive overview of the latest discoveries in a particular field, or presenting a personal perspective on recent developments, are also welcome. See the full list of article types we accept for more information.
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