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

699
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:
699
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
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...
335
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

54
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...
54
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.2K
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:
1.2K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

926
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
926
Introduction to Epidemiology01:26

Introduction to Epidemiology

2.5K
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,...
2.5K

You might also read

Related Articles

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

Sort by
Same author

The big picture : Parasites, People and the Path to Ending Schistosomiasis.

EMBO reports·2026
Same author

Epidemiological impacts of nonpharmaceutical interventions are modulated by immunity exposure trade offs.

Communications medicine·2026
Same author

A complete catalogue of human-infective RNA viruses.

Scientific data·2026
Same author

Spatio-temporal modelling of in vitro influenza A virus infection: The impact of defective interfering particles on the type I interferon response.

PLoS computational biology·2026
Same author

Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus.

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

Prevalence and diversity of gastro-intestinal nematode infections in British cattle and implications for biosecurity.

Veterinary parasitology·2026

Related Experiment Video

Updated: Apr 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K

INFERENCE FOR INDIVIDUAL-LEVEL MODELS OF INFECTIOUS DISEASES IN LARGE POPULATIONS.

Rob Deardon1, Stephen P Brooks2, Bryan T Grenfell3

  • 1Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.

Statistica Sinica
|September 26, 2015
PubMed
Summary

Individual Level Models (ILMs) offer insights into infectious disease spread. A new method addresses computational challenges for large datasets, improving epidemic modeling for diseases like foot-and-mouth disease (FMD).

Keywords:
Bayesian inferenceMarkov chain Monte CarloSpatio-temporal epidemic modellingcomputational methodologyfoot-and- mouth diseasemissing data

More Related Videos

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

3.3K
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.9K

Related Experiment Videos

Last Updated: Apr 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
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

3.3K
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.9K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Individual Level Models (ILMs) are increasingly used for infectious disease dynamics.
  • Bayesian parameterization via Markov chain Monte Carlo (MCMC) is common but computationally intensive.
  • Large datasets and missing data pose significant challenges for ILM parameterization.

Purpose of the Study:

  • To present a methodology for estimating parameters in large and/or incomplete infectious disease datasets.
  • To overcome computational limitations associated with traditional ILM parameterization.
  • To apply the methodology to the UK 2001 foot-and-mouth disease (FMD) epidemic.

Main Methods:

  • Development of an efficient parameter estimation methodology for ILMs.
  • Application of Bayesian inference with computational optimizations.
  • Utilizing the UK 2001 FMD epidemic data for case study.

Main Results:

  • Successfully estimated parameters for a large, incomplete dataset.
  • Demonstrated the feasibility of the proposed methodology for complex epidemic scenarios.
  • Provided insights into the spatio-temporal dynamics of the FMD outbreak.

Conclusions:

  • The developed methodology effectively addresses computational challenges in ILM parameterization.
  • This approach enhances the applicability of ILMs to large-scale, real-world epidemic data.
  • The findings contribute to improved understanding and management of infectious disease outbreaks.