Jove
Visualize
联系我们

相关概念视频

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

349
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
349
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36
Causality in Epidemiology01:21

Causality in Epidemiology

383
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
383
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
68
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

122
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
122
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

128
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
128

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Evaluating the effectiveness of vaccination campaigns: Insights from unvaccinated mortality data.

Infectious Disease Modelling·2025
Same author

The 1978 English boarding school influenza outbreak: where the classic SEIR model fails.

Journal of the Royal Society, Interface·2024
Same author

Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies.

BMC medical research methodology·2024
Same author

Assessing the impact of Australia's mass vaccination campaigns over the Delta and Omicron outbreaks.

PloS one·2024
Same author

Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Detection of grey zones in inter-rater agreement studies.

BMC medical research methodology·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 23, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

空间时间流行病学建模的JAGS模型规范.

Dinah Jane Lope1, Haydar Demirhan1

  • 1School of Science, Mathematical Sciences Discipline, RMIT University, Melbourne, 3000, Victoria, Australia.

Spatial and spatio-temporal epidemiology
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

使用吉布斯采样 (BUGS) 的贝叶斯推理是传染病建模的关键. 本研究比较了Just Another Gibbs Sampler (JAGS) 中的两种策略,以提高复杂模型的计算效率.

关键词:
错误 错误 是一个错误.贝叶斯模型是贝叶斯模型.计算时间计算时间效率 效率是指效率是指效率.流行病学模型 流行病学模型吉布斯采样仪 吉布斯采样仪传染病模型的传染病模型.这就是 JAGS 的原因.运行时间 运行时间时间空间模型.在WinBUGS中,我们可以使用WinBUGS.

更多相关视频

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

相关实验视频

Last Updated: Jun 23, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

科学领域:

  • 流行病学 流行病学
  • 计算统计学 计算统计学
  • 传染病的动态传染病的动态.

背景情况:

  • 使用吉布斯采样 (BUGS) 的贝叶斯推理在过去二十年的传染病建模中变得很突出.
  • 马尔科夫链蒙特卡洛 (MCMC) 方法的整合使贝叶斯分析在这个领域普及.
  • 复杂的传染病模型具有时空元件和众多参数,对现有的MCMC软件构成计算挑战.

研究的目的:

  • 调查和比较两个替代代订阅策略的性能,在"只是另一个吉布斯采样器" (JAGS) 环境中创建模型.
  • 评估这些策略对贝叶斯空间时间传染病模型计算运行时间的影响.

主要方法:

  • 在JAGS中实施两种不同的订阅策略来定义模型.
  • 使用复杂的传染病模型,对与每个策略相关的运行时间进行实证评估.
  • 专注于包含空间和时间依赖以及多个参数的模型.

主要成果:

  • 该研究发现,这两种被调查的订阅策略之间的运行时间存在显著差异.
  • 一种策略在测试模型的计算效率方面表现出优异的性能.
  • 这些发现为优化JAGS.中的模型实现提供了实用见解.

结论:

  • 在JAGS中选择订阅策略可以显著影响贝叶斯空间时空传染病建模的效率.
  • 实践者可以利用这些发现来选择更有效的建模方法,确保及时分析.
  • 这项研究有助于在流行病学研究中应用先进的计算技术.