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

244
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:
244
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

5.7K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
5.7K
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

589
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:
589
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
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Impact of Antiviral Therapy Scale-Up Among People Who Inject Drugs in Scotland: Regional Evidence of Hepatitis C Virus Elimination.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

Application of machine-learning algorithms to identify the key determinants of risk for HIV, hepatitis C and hepatitis B in primary care settings.

BMC infectious diseases·2026
Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

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

Editorial: Improving pandemic and epidemic responses - novel methods and lessons learned from previous infectious disease outbreaks.

Journal of theoretical biology·2026
Same author

ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis.

Epidemics·2026
Same author

Infectious disease outbreak controllability: biological, social and public health factors.

Proceedings. Biological sciences·2026
Same journal

Spatio-temporal modeling of zoonotic cutaneous leishmaniasis (ZCL) in the Algerian steppe: Epidemiological insights and climatic associations.

Epidemics·2026
Same journal

Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review.

Epidemics·2026
Same journal

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

Epidemics·2026
Same journal

Mapping the landscape of individual-based models for respiratory pathogen transmission in the pandemic and post-pandemic era (2020-2024): A systematic review.

Epidemics·2026
Same journal

A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.

Epidemics·2026
Same journal

Modelling serological cross-reactivity to disentangle the dynamics of West Nile and Usutu viruses in an emerging area.

Epidemics·2026
See all related articles

Related Experiment Video

Updated: Oct 3, 2025

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.8K

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.

Ben Swallow1, Paul Birrell2, Joshua Blake3

  • 1School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK.

Epidemics
|February 18, 2022
PubMed
Summary
This summary is machine-generated.

Estimating infectious disease models faces challenges in uncertainty quantification, data issues, inference, and expert judgment. Addressing these is crucial for pandemic preparedness and effective policy.

Keywords:
Expert elicitationPandemic modellingStatistical estimationUncertainty quantification

More Related Videos

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.4K
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.3K

Related Experiment Videos

Last Updated: Oct 3, 2025

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.8K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.4K
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.3K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health Policy

Background:

  • The COVID-19 pandemic underscored the critical need for accurate infectious disease modeling.
  • Rapid and reliable data-model integration for policy decisions remains a significant challenge.

Purpose of the Study:

  • To identify and discuss key challenges in infectious disease model parameter and structure estimation.
  • To propose priorities for improving estimation methodologies for future pandemic preparedness.

Main Methods:

  • Review and discussion of four core challenges in infectious disease estimation: Uncertainty Quantification (UQ), data challenges, model-based inference and prediction, and expert judgment.
  • Identification of key areas for methodological advancement.

Main Results:

  • Highlighted significant hurdles in applying UQ frameworks to infectious disease models.
  • Discussed limitations in data availability, quality, and integration for real-time estimation.
  • Examined complexities in model-based inference and prediction accuracy.
  • Addressed the role and challenges of incorporating expert judgment into modeling.

Conclusions:

  • Robust infectious disease modeling requires addressing estimation challenges in UQ, data, inference, and expert judgment.
  • Prioritizing methodological advancements in these areas is essential for enhancing pandemic response and future preparedness.