Jove
Visualize
联系我们
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

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

152
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:
152
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

411
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:
411
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.3K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

223
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
223
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

124
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
124

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

Longitudinal assessment and predictors of subjective taste change after hematopoietic cell transplantation (HCT).

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Correlates of Fitness Tracker Ownership and Use in Cancer Survivors: Cross-Sectional Survey.

JMIR cancer·2026
Same author

How Big Is the "Gray Area"? Navigating Health-Threatening Previability Pregnancy Complications in States With Abortion Restrictions.

Annals of internal medicine·2026
Same author

Assessing mpox knowledge and sexual behaviours within high-risk populations in the Democratic Republic of the Congo.

BMJ global health·2026
Same author

Why Americans are Dying Younger? NIH Is Not the Problem. Our Broken Healthcare Delivery Is.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2025
Same author

Why Americans are Dying Younger? NIH Is Not the Problem. Our Broken Healthcare Delivery Is.

The Journal of infectious diseases·2025

相关实验视频

Updated: Jul 19, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.8K

使用强盗算法最大化SARS-CoV-2病例发现:评估和可行性研究

Michael F Rayo1, Daria Faulkner2, David Kline3

  • 1Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States.

JMIR public health and surveillance
|August 15, 2023
PubMed
概括

这项研究成功地使用了贝叶斯搜索算法来监测COVID-19,最大限度地检测病例并接触到服务不足的社区. 这种创新方法加强了城市环境中的传染病监测.

关键词:
在 COVID-19 疫情中,这就是SARS-CoV-2病毒.积极监督是指积极的监督.盗算法 盗算法 盗算法社区健康 社区健康传染病是一种传染性疾病.强化学习是一种强化学习.

更多相关视频

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots
11:11

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots

Published on: February 11, 2022

4.6K
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K

相关实验视频

Last Updated: Jul 19, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.8K
Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots
11:11

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots

Published on: February 11, 2022

4.6K
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K

科学领域:

  • 流行病学 流行病学
  • 公共卫生 公共卫生
  • 生物统计学 生物统计学

背景情况:

  • 灵活自适应算法监测测试 (FAAST) 计划开创了积极疾病监测的贝叶斯自适应方法.
  • 迫切需要检测SARS-CoV-2病例,特别是可用的COVID-19治疗方法,推动了这项研究.
  • 贝叶斯搜索算法虽然在其他领域使用,但对于积极的传染病监测来说是新的.

研究的目的:

  • 评估贝叶斯搜索算法,以准俄俄州哥伦布的SARS-CoV-2传播热点.
  • 为了最大限度地检测新的SARS-CoV-2病例随着时间的推移在各种社区地点.
  • 评估适应算法在现实世界公共卫生监测中的可行性.

主要方法:

  • 一个基于普森采样的贝叶斯搜索算法指导了弹出的SARS-CoV-2测试.
  • 在俄俄州哥伦布的13个不同地点建立了弹出测试站点.
  • 礼品卡和快速抗原测试等激励措施鼓励参与测试活动.

主要成果:

  • 贝叶斯算法有效地将测试引导到具有较高SARS-CoV-2病例产量的位置.
  • 该战略出乎意料地最大限度地提高了服务不足社区的少数民族居民的病例识别.
  • 测试努力成功地超出了非洲裔美国参与者的比例,相对于当地人口统计数据.

结论:

  • 使用强盗算法进行弹出测试是一种可行的城市流行病战略.
  • 这标志着这些算法在疾病监测中的首次现实应用.
  • 该研究验证了自适应算法的有效性,用于检测未被诊断的SARS-CoV-2和其他感染.