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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

185
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...
185
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

414
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:
414
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

799
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:
799
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

222
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
222
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

199
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...
199
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.5K
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,...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Adaptive and sequential cancer therapies emerge from treatment schedule optimization.

Research square·2026
Same author

Cancer During Pregnancy: Navigating Clinical and Research Challenges.

Current oncology (Toronto, Ont.)·2026
Same author

Invest where impact begins: recommendations from Breast Cancer Research Foundation Early Career Investigator Working Group (Part 1 of 2).

NPJ breast cancer·2026
Same author

CLO26-115: Association Between Geriatric Assessment Variables and Treatment Completion in Older Adults With Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy and Immunotherapy: The MSKCC Experience.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

CLO26-115: Association Between Geriatric Assessment Variables and Treatment Completion in Older Adults With Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy and Immunotherapy: The MSKCC Experience.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Homologous recombination deficiency and hemizygosity drive resistance in breast cancer.

Nature·2026

Related Experiment Video

Updated: Dec 19, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Fractional SIR Epidemiological Models.

Amirhossein Taghvaei1, Tryphon T Georgiou1, Larry Norton2

  • 1Department of Mechanical & Aerospace Engineering, University of Calfornia, Irvine, CA 92697-3975.

Medrxiv : the Preprint Server for Health Sciences
|June 9, 2020
PubMed
Summary
This summary is machine-generated.

This study proposes epidemiological models using fractional exponents for disease transmission rates, challenging standard assumptions. The findings suggest transmission depends on fractional powers, better reflecting localized spread dynamics.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.9K

Related Experiment Videos

Last Updated: Dec 19, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.9K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • Standard epidemiological models assume transmission rate is proportional to the product of susceptible and infected populations.
  • This assumption relies on strong mixing and widespread contact, which may not accurately represent early epidemic phases.

Conclusions:

  • Fractional exponent epidemiological models provide a more nuanced representation of disease transmission, especially in early epidemic stages and spatially structured populations.
  • The findings suggest that transmission dynamics may be better described by fractional powers rather than simple products of population densities.
  • This work opens new avenues for modeling infectious diseases with complex contact patterns.