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

102
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:
102
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
38
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

23
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...
23
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

81
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
81
Censoring Survival Data01:09

Censoring Survival Data

55
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
55
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

475
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
475

You might also read

Related Articles

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

Sort by
Same author

Wavelength Selection for Periodic Travelling Waves: An Unsolved Problem.

Bulletin of mathematical biology·2026
Same author

An Economic Evaluation of the Adjuvanted Quadrivalent Influenza Vaccine Compared with Standard-Dose Quadrivalent Influenza Vaccine in the Spanish Older Adult Population.

Vaccines·2022
Same author

A geometric analysis of the SIRS epidemiological model on a homogeneous network.

Journal of mathematical biology·2021
See all related articles

Related Experiment Video

Updated: May 22, 2025

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

2.3K

A minimal model for multigroup adaptive SIS epidemics.

Massimo A Achterberg1, Mattia Sensi2,3, Sara Sottile4

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands.

Chaos (Woodbury, N.Y.)
|March 14, 2025
PubMed
Summary

This study introduces a multigroup adaptive N-Intertwined Mean-Field Approximation (aNIMFA) model to analyze disease spread in networks. The model reveals that community connections impact disease dynamics, offering insights for public health interventions.

More Related Videos

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

10.6K
Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

9.8K

Related Experiment Videos

Last Updated: May 22, 2025

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

2.3K
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

10.6K
Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

9.8K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Network Science

Background:

  • The adaptive N-Intertwined Mean-Field Approximation (aNIMFA) model analyzes disease spread in networks.
  • Understanding disease dynamics in complex, heterogeneous networks is crucial for effective public health strategies.

Purpose of the Study:

  • To generalize the aNIMFA model to heterogeneous networks of communities.
  • To investigate the influence of local and global disease awareness on disease transmission.
  • To analyze the existence and stability of system equilibria using the basic reproduction number (R0).

Main Methods:

  • Developed a multigroup aNIMFA model for heterogeneous networks.
  • Analyzed the existence and stability of equilibria based on R0.
  • Conducted numerical simulations to explore disease dynamics and intervention strategies.

Main Results:

  • The basic reproduction number (R0) in this model aligns with static network models when no disease-induced contact reduction occurs.
  • Periodic disease behavior emerged in simulations with just two communities, unlike single-community models.
  • Disrupting inter-community links proved more effective than intra-community link disruption for reducing outbreaks in dense networks.

Conclusions:

  • The multigroup aNIMFA model provides a framework for understanding disease spread in complex networks with varying awareness levels.
  • Network structure and community interactions significantly influence epidemic trajectories.
  • The adaptive modeling approach has broad applicability to various epidemiological compartmental models beyond Susceptible-Infected-Susceptible (SIS).