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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

137
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...
137
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

242
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
242
Causality in Epidemiology01:21

Causality in Epidemiology

1.0K
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...
1.0K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

587
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:
587
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

243
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:
243

You might also read

Related Articles

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

Sort by
Same author

A reduced basis decomposition approach to efficient data collection in pairwise comparison studies.

Computational statistics·2026
Same author

Scalable Bayesian inference for bradley-Terry models with ties: an application to honour based abuse.

Journal of applied statistics·2025
Same author

Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data.

Statistics in medicine·2020
See all related articles

Related Experiment Video

Updated: Oct 1, 2025

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.0K

Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

Rowland G Seymour1, Theodore Kypraios2, Philip D O'Neill2

  • 1Rights Lab, University of Nottingham, Nottingham, NG7 2RD United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|March 3, 2022
PubMed
Summary

This study introduces a flexible Bayesian nonparametric framework for infectious disease modeling, offering a data-driven approach to understand transmission mechanisms and avoid erroneous conclusions from traditional parametric models.

Keywords:
disease transmission modelsfoot and mouth diseasemultioutput Gaussian processesspatial epidemic models

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

2.7K
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

326

Related Experiment Videos

Last Updated: Oct 1, 2025

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.0K
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

2.7K
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

326

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Biostatistics

Background:

  • Mathematical models are crucial for understanding and controlling infectious disease outbreaks.
  • Traditional parametric models rely on potentially unjustified assumptions about transmission dynamics.
  • These assumptions can lead to inaccurate scientific conclusions and predictions.

Purpose of the Study:

  • To propose a flexible Bayesian nonparametric framework for infectious disease modeling.
  • To enable a more data-driven approach to inferring transmission mechanisms.
  • To enhance understanding of disease transmission dynamics, exemplified by the 2001 UK foot and mouth disease outbreak.

Main Methods:

  • Development of a Bayesian nonparametric framework.
  • Application of the framework to analyze transmission mechanisms.
  • Utilizing real-world outbreak data for model inference.

Main Results:

  • The proposed framework reduces reliance on strict, potentially unjustified, parametric assumptions.
  • It facilitates a more data-driven inference of disease transmission processes.
  • Enhanced understanding of the transmission dynamics of the 2001 UK foot and mouth disease outbreak was achieved.

Conclusions:

  • Bayesian nonparametric methods offer a more flexible and robust approach to infectious disease modeling.
  • This data-driven strategy improves the accuracy of understanding and predicting disease transmission.
  • The framework has significant implications for outbreak mitigation and prevention strategies.