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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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:
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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...
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
Causality in Epidemiology01:21

Causality in Epidemiology

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...

You might also read

Related Articles

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

Sort by
Same author

Disease-modifying antirheumatic drugs (DMARDs) for rheumatoid arthritis after failure of biologic or targeted synthetic therapy: a systematic review and network meta-analysis.

The Cochrane database of systematic reviews·2026
Same author

MRSA transmission in hospitals across Alberta, Canada: a comparative study combining unidentified colonized cases upon admission.

BMC infectious diseases·2026
Same author

Memory mechanisms for behavioural change in Bayesian individual-level spatial epidemic models.

Infectious Disease Modelling·2026
Same author

Identifying memory mechanisms in Bayesian models of behavioural change during epidemics.

Epidemics·2026
Same author

Spatio-temporal spread of COVID-19 over three variant waves in the continental United States.

Proceedings. Biological sciences·2026
Same author

Impact of Hepatitis C screening and treatment among incarcerated populations in Alberta, Canada on population-level Hepatitis C elimination efforts.

The International journal on drug policy·2026

Related Experiment Video

Updated: May 8, 2026

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

Spatial approximations of network-based individual level infectious disease models.

Nadia Bifolchi1, Rob Deardon, Zeny Feng

  • 1Department of Mathematics & Statistics, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada. nbifolch@uoguelph.ca

Spatial and Spatio-Temporal Epidemiology
|August 27, 2013
PubMed
Summary

Spatial models for infectious disease spread work well for local contacts but struggle with long-distance transmission. Network models offer better predictions when disease spreads widely across complex contact networks.

Keywords:
Contact networkEpidemic modelingILMIndividual-level modelsMCMCMarkov chain Monte CarloSARSSIRSpatial approximationindividual-level modelsevere acute respiratory syndromesusceptible-infected-removed

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: May 8, 2026

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

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Epidemiology
  • Computational Biology
  • Network Science

Background:

  • Infectious disease modeling often simplifies complex contact networks using spatial data.
  • This simplification may impact the accuracy of predicting disease spread.

Purpose of the Study:

  • To evaluate the predictive performance of spatial models versus network models for infectious disease spread.
  • To identify conditions under which spatial models fail to accurately predict infection probabilities.

Main Methods:

  • Simulated epidemic spread across diverse contact networks.
  • Applied spatial-based, individual-level models within a Bayesian framework.
  • Utilized Markov chain Monte Carlo (MCMC) methods for model fitting.

Main Results:

  • Spatial models accurately predict infection probabilities when disease transmission occurs primarily through local contacts.
  • Performance of spatial models degrades significantly when contacts span longer distances.
  • Network models provide superior predictions in scenarios with extensive long-distance contacts.

Conclusions:

  • The choice of model (spatial vs. network) is crucial and depends on the underlying contact structure of disease propagation.
  • Spatial models are a useful approximation for localized transmission but are limited by the scale of contact networks.