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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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:
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
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)...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...

You might also read

Related Articles

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

Sort by
Same author

Antibiotic prescribing in canine infectious respiratory disease.

Preventive veterinary medicine·2026
Same author

Understanding the effects of reductions in local government expenditure on food safety services in England, 2009-10 to 2019-20: a longitudinal ecological study.

BMJ open·2026
Same author

<i>Mycobacterium tuberculosis</i> bacillus induces pyroptosis in human lung fibroblasts.

mSphere·2025
Same author

A framework for handling uncertainty in a large-scale programme estimating the Global Burden of Animal Diseases.

Frontiers in veterinary science·2025
Same author

Inequalities in local government expenditure on environmental and regulatory services in England from 2009 to 2020: a longitudinal ecological study.

BMJ public health·2025
Same author

Effectiveness of a digital health and financial incentive intervention to promote physical activity in patients with type 2 diabetes: study protocol for a randomised controlled trial with a nested qualitative study-ACTIVATE trial.

Trials·2024

Related Experiment Video

Updated: Jun 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Case studies in Bayesian microbial risk assessments.

Marc C Kennedy1, Helen E Clough, Joanne Turner

  • 1Food and Environment Research Agency, Sand Hutton, York, UK. marc.kennedy@fera.gsi.gov.uk

Environmental Health : a Global Access Science Source
|January 28, 2010
PubMed
Summary
This summary is machine-generated.

Bayesian statistics effectively quantify uncertainty in foodborne microbial risk analysis. This approach integrates diverse data for realistic risk estimates, as shown in two food safety case studies.

More Related Videos

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products
08:19

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Related Experiment Videos

Last Updated: Jun 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products
08:19

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Area of Science:

  • Food safety
  • Quantitative risk analysis
  • Bayesian statistics

Background:

  • Quantifying uncertainty is crucial for accurate quantitative risk analysis.
  • Bayesian statistics offer a robust framework for integrating diverse data sources and quality.
  • This facilitates realistic estimation of combined uncertainties in risk assessments.

Purpose of the Study:

  • To demonstrate the application of Bayesian statistics in foodborne microbial risk assessment.
  • To integrate multiple data sources and quantify uncertainty in risk estimations.
  • To perform efficient sensitivity analyses for complex risk models.

Main Methods:

  • Two case studies involving foodborne microbial risks (VTEC O157 in milk and dairy herd prevalence).
  • Monte Carlo simulations for uncertainty propagation in farm and pasteurization models.
  • Bayesian sensitivity analysis using statistical approximations of computer code.

Main Results:

  • Estimated 8.6 yearly illnesses in young children from VTEC O157 in milk (95% UI: 0-11.5).
  • Banning on-farm pasteurization reduced estimated illnesses to 6.4 (95% UI: 0-11).
  • Sensitivity analysis reduced 30 inputs to 7, with 2 inputs explaining 82.8% of output variance.

Conclusions:

  • Bayesian statistics provide a rigorous and efficient method for uncertainty and sensitivity analysis in risk assessment.
  • The approach successfully integrates multiple information sources for comprehensive risk evaluation.
  • Demonstrated utility in practical food safety scenarios.