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

Causality in Epidemiology01:21

Causality in Epidemiology

834
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...
834
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

204
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:
204
Introduction to Epidemiology01:26

Introduction to Epidemiology

996
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
996
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

533
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:
533
Observational Learning01:12

Observational Learning

312
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
312
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86

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

An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System.

Computational brain & behavior·2026
Same author

Modeling the disruptive impact of the COVID-19 pandemic on nurses' supply and wages.

Frontiers in epidemiology·2026
Same author

Mobilising community-managed organisations: A call to action to confront physical health inequities in people living with complex mental health challenges.

The Australian and New Zealand journal of psychiatry·2026
Same author

Uncertainty Aware Decision Support with Computationally Expensive Simulation Models: A Case Study of HIV Intervention Scenarios.

medRxiv : the preprint server for health sciences·2026
Same author

Assessing head injury risk and neuroprotective effect of ketone monoester supplementation in military airborne training.

Physiological reports·2026
Same author

Human DEAH-box helicase 8 regulates HSF1-mediated stress response and cancer-associated pre-mRNA splicing in tumour cells.

NAR cancer·2026

相关实验视频

Updated: Sep 11, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K

在基于流行病学剂的模拟中,认知上可信的强化学习.

Konstantinos Mitsopoulos1, Lawrence Baker2, Christian Lebiere3

  • 1Florida Institute for Human and Machine Cognition, Pensacola, FL, United States.

Frontiers in epidemiology
|August 12, 2025
PubMed
概括

这项研究引入了流行病学模型的新框架,该模型利用认知原则整合了人类行为. 它显示了当地的社会线索如何强烈地影响公共卫生遵守,比如在流行病期间戴口罩.

关键词:
在ACT-R中,它是ACT-R.基于代理的建模.认知建模的认知建模传染病建模传染病建模强化学习是一种强化学习.

更多相关视频

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.6K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.1K

相关实验视频

Last Updated: Sep 11, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K
A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.6K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.1K

科学领域:

  • 计算流行病学计算流行病学
  • 认知科学是一种认知科学.
  • 公共卫生建模公共卫生建模

背景情况:

  • 人类行为显著影响传染病的传播和公共卫生干预的有效性.
  • 传统的基于代理的模型 (ABM) 经常过度简化行为动态,限制了它们的准确性.
  • 将复杂的决策整合到模拟中对于现实的流行病学建模至关重要.

研究的目的:

  • 为基于代理的模型 (ABM) 开发一种新的框架,该框架包含认知上可信的强化学习 (RL).
  • 在没有广泛的数据训练的情况下,在模拟中实现动态行为适应.
  • 提高流行病学模拟的准确性和可解释性.

主要方法:

  • 提出了一个框架,将思维-理性的自适应控制 (ACT-R) 和基于实例的学习 (IBL) 结合起来,用于ABM中的非参数RL.
  • 在COVID-19大流行期间模拟了戴口罩的行为,以证明该框架的实用性.
  • 分析了当地与全球社会线索对行为和疾病传播的影响.

主要成果:

  • 当地社会线索与集群口罩佩戴行为有很强的相关性 (斜率=0.54,r=0.76).
  • 仅仅依赖全球线索就导致了弱分类模式 (斜率=0.05,r=0.09).
  • 在模拟中证明了框架的可扩展性和认知解释性.

结论:

  • 新的框架有效地将适应性决策融入流行病学模拟中.
  • 当地信息在协调公共卫生合规方面发挥着至关重要的作用.
  • 该方法为公共卫生政策和干预策略提供了可操作的见解.