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

122
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:
122
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

37
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...
37
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

533
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...
533
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

129
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
129
Survival Tree01:19

Survival Tree

79
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
79

You might also read

Related Articles

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

Sort by
Same author

Data-driven, ML-assisted approaches to problem well-posedness.

PNAS nexus·2026
Same author

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations.

PNAS nexus·2025
Same author

On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them.

PNAS nexus·2024
Same author

Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Chaos (Woodbury, N.Y.)·2024
Same author

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold.

Nature communications·2024
Same author

Task-oriented machine learning surrogates for tipping points of agent-based models.

Nature communications·2024

Related Experiment Video

Updated: Jun 24, 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

2.4K

Tipping points of evolving epidemiological networks: Machine learning-assisted, data-driven effective modeling.

Nikolaos Evangelou1, Tianqi Cui1, Juan M Bello-Rivas1

  • 1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.

Chaos (Woodbury, N.Y.)
|June 12, 2024
PubMed
Summary

This study uses machine learning to model tipping points in adaptive epidemiological networks. It identifies a novel effective stochastic differential equation revealing subcritical Hopf bifurcations and rare, large collective oscillations.

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

10.7K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

167

Related Experiment Videos

Last Updated: Jun 24, 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

2.4K
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

10.7K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

167

Area of Science:

  • Complex Systems
  • Epidemiology
  • Network Science

Background:

  • Adaptive epidemiological networks exhibit complex dynamics, including tipping points.
  • Understanding these tipping points is crucial for predicting disease spread and network behavior.

Purpose of the Study:

  • To investigate tipping point collective dynamics in adaptive susceptible-infected-susceptible (SIS) networks using a data-driven approach.
  • To identify an effective stochastic differential equation (eSDE) that captures the network's coarse-grained behavior.

Main Methods:

  • Employed a deep-learning ResNet architecture inspired by numerical stochastic integrators to identify the eSDE.
  • Constructed an approximate effective bifurcation diagram from the eSDE's drift term.
  • Utilized manifold learning techniques, specifically Diffusion Maps, for data-driven observable identification.

Main Results:

  • Identified a parameter-dependent eSDE capturing the network's dynamics.
  • Observed a subcritical Hopf bifurcation leading to tipping point behavior characterized by rare, large-amplitude collective oscillations.
  • Successfully identified the collective SDE and performed rare event computations using data-driven coarse observables.

Conclusions:

  • The study reveals a subcritical Hopf bifurcation as the mechanism for tipping points in adaptive SIS networks.
  • The developed machine learning framework effectively models complex dynamics and tipping phenomena.
  • The methodology is broadly applicable to other complex dynamic systems exhibiting tipping point behavior.