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:
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
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...
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

Prevalence of sarcopenia in patients with rheumatoid arthritis: a cross-sectional study.

RMD open·2026
Same author

Identification of potential biomarkers related to mannose metabolism in keloids: analysis of integrated bulk RNA-seq and scRNA-seq.

Frontiers in immunology·2026
Same author

Arid3b suppresses CD8 + T cell infiltration and function in microsatellite-stable colorectal cancer via Runx3.

Nature communications·2026
Same author

Evaluation and estimation of epidemic trajectories for SARS-CoV-2 from clinical and wastewater data in Gauteng Province, South Africa.

PLOS global public health·2026
Same author

Phage therapy: A novel strategy to combat drug-resistant Salmonella Pullorum infection in chickens.

Veterinary microbiology·2026
Same author

Artificial intelligence-based multimodal multitask analysis of thyroid ultrasound image features predicts thyroid cancer: a multicenter study.

JNCI cancer spectrum·2026

Related Experiment Videos

On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2.

Trevelyan J McKinley1, Daniel B Williamson2, Xiaoyu Xiong2

  • 1University of Exeter Medical School, University of Exeter, Exeter, United Kingdom.

Plos Computational Biology
|June 1, 2026
PubMed
Summary

This study introduces a new framework for calibrating complex infectious disease models, like those for COVID-19 transmission. It uses emulation and particle filtering to efficiently handle large datasets and model complexities.

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Complex stochastic infectious disease models present calibration challenges due to high-dimensional spaces and non-linear dynamics.
  • Paucity of data leads to unignorable hidden states, complicating inference routines.
  • Existing methods like likelihood-based approaches and direct simulation have scalability limitations.

Purpose of the Study:

  • To present a novel framework for calibrating large-scale, stochastic, age-structured, spatial meta-population models.
  • To address the challenges of high-dimensional data and hidden states in infectious disease modeling.
  • To improve the efficiency and applicability of infectious disease model calibration.

Main Methods:

  • Development of an emulation-based framework.
  • Embedding a model discrepancy process within the simulation model.
  • Integration with particle filtering to emulate the log-likelihood surface.

Main Results:

  • Successful calibration of a COVID-19 transmission model for England and Wales.
  • Demonstration of emulating the log-likelihood surface for efficient calibration.
  • Alleviation of challenges related to spatial and temporal infection introduction.

Conclusions:

  • The proposed framework enables calibration of complex models to high-dimensional data.
  • Emulation-based methods offer a scalable solution for infectious disease model calibration.
  • Further research is needed to address remaining challenges in model calibration and application.