Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Intelligence01:27

Intelligence

8.7K
The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
8.7K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.9K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.9K
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

2.2K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
2.2K
Relative Risk01:12

Relative Risk

2.2K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.2K
Measures of Intelligence01:29

Measures of Intelligence

8.6K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.6K
Multiple Intelligences Theory01:20

Multiple Intelligences Theory

9.1K
Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.
9.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Climate-driven flooding widens economic inequalities across EU regions.

Scientific reports·2026
Same author

Steering open-source AI to accelerate the sustainable development goals.

Nature communications·2026
Same author

Who Talks About Flood Risks and Climate Change Adaptation? Analysis of Social Interactions in Three Countries.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

How an economic and financial perspective could guide transformational adaptation to sea level rise.

npj climate action·2025
Same author

The power of bridging decision scales: Model coupling for advanced climate policy analysis.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Private investments in climate change adaptation are increasing in Europe, although sectoral differences remain.

Communications earth & environment·2025

Related Experiment Video

Updated: Feb 13, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.1K

Intelligent judgements over health risks in a spatial agent-based model.

Shaheen A Abdulkareem1,2, Ellen-Wien Augustijn3, Yaseen T Mustafa4

  • 1Department of Governance and Technology for Sustainability (CSTM), Faculty of Behavioral, Management, and Social Sciences (BMS), University of Twente, Enschede, The Netherlands. s.a.abdulkareem@utwente.nl.

International Journal of Health Geographics
|March 22, 2018
PubMed
Summary
This summary is machine-generated.

Integrating machine learning into agent-based models enhances disease spread simulations by capturing human risk perception and protective behaviors. This approach improves understanding of infectious disease dynamics and intervention strategies.

Keywords:
Bayesian networksCholeraDisease diffusionEmergent behaviorLearningProtection motivation theory

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.5K

Related Experiment Videos

Last Updated: Feb 13, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.5K

Area of Science:

  • Computational epidemiology
  • Behavioral science
  • Machine learning

Background:

  • Infectious diseases pose a global threat, necessitating better predictive models.
  • Current agent-based models (ABMs) often overlook the crucial role of risk perception in disease spread.
  • Understanding how individuals perceive risk and adapt behavior is key to controlling epidemics.

Purpose of the Study:

  • To develop an innovative agent-based model (ABM) integrating machine learning (ML) for behavioral dynamics.
  • To simulate the impact of risk perception and adaptive behaviors on disease transmission.
  • To compare disease spread patterns between intelligent and non-intelligent agents.

Main Methods:

  • Developed a spatial agent-based model (ABM) incorporating Protection Motivation Theory.
  • Integrated two Bayesian Networks (BNs) into a NetLogo-based Cholera ABM: BN1 for risk perception, BN2 for risk and coping behavior.
  • Conducted computational experiments comparing zero-intelligent agents with agents exhibiting BN1 and BN2 intelligence.

Main Results:

  • Simulated epidemic curves, risk perception dynamics, and coping strategy distributions for different agent intelligence levels.
  • Demonstrated significant differences in disease spread patterns based on agent behavior.
  • Quantified the impact of intelligent decision-making on disease incidence.

Conclusions:

  • Integrating behavioral decision-making using ML into spatial ABMs is crucial for accurate disease modeling.
  • This approach enhances the study of intervention strategies and the cumulative effects of behavioral changes.
  • The findings highlight the importance of human behavior in infectious disease dynamics.