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

696
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:
696
Modeling with Differential Equations01:25

Modeling with Differential Equations

220
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
220
Causality in Epidemiology01:21

Causality in Epidemiology

2.0K
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...
2.0K
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

427
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
427
Population Growth00:57

Population Growth

29.5K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
29.5K
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

47
Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable...
47

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

Probabilistic Cardiac Digital Twins for Robust Patient-Specific Modeling.

bioRxiv : the preprint server for biology·2026
Same author

Biochemical implementation of acceleration sensing and PIDA control.

NPJ systems biology and applications·2025
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

Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning.

BMC bioinformatics·2024
Same journal

MGF100 but not MGF300 family is a potential multigene-deleted target for ASFV attenuation and live attenuated vaccine development.

Virulence·2026
Same journal

<i>Parabacteroides distasonis</i> alleviates <i>Clostridioides difficile</i> infection in mice while modulating secondary bile acids.

Virulence·2026
Same journal

Identification and profiling of HLA-A*02:01-restricted <i>Toxoplasma gondii</i> peptides through immunopeptidomics in HLA-A2.1 transgenic mice.

Virulence·2026
Same journal

Encephalomyocarditis virus impairs the blood-brain barrier by degrading tight junction proteins via AKT3-dependent autophagic and apoptotic pathways.

Virulence·2026
Same journal

A secreted AIP-Like peptide from <i>Helcococcus kunzii</i> inhibits the Agr quorum sensing system of <i>Staphylococcus aureus</i>.

Virulence·2026
Same journal

Correction.

Virulence·2026
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

37.2K

Modeling epidemics on adaptively evolving networks: A data-mining perspective.

Assimakis A Kattis1, Alexander Holiday1, Ana-Andreea Stoica2

  • 1a Department of Chemical and Biological Engineering ; Princeton University ; Princeton , NJ USA.

Virulence
|December 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces Diffusion Maps (DMAPS) to identify key network statistics for understanding epidemic spread. This data-driven method simplifies complex epidemic models on adaptive networks.

Keywords:
SISadaptive networksdata miningdiffusion mapsepidemicsequation-free

More Related Videos

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

3.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.3K

Related Experiment Videos

Last Updated: Mar 28, 2026

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

37.2K
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

3.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.3K

Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Modeling epidemic dynamics on adaptive networks presents challenges in identifying informative summary statistics.
  • Existing models struggle to coarse-grain complex network epidemic data into accurate, reduced-size macroscopic models.

Purpose of the Study:

  • To develop a data-based approach for detecting informative collective observables in detailed epidemic dynamics.
  • To address the challenge of summarizing complex network states for macroscopic modeling.

Main Methods:

  • Utilized Diffusion Maps (DMAPS), a data-mining technique, to identify key collective observables.
  • Applied the method to simulations of a mathematical model of epidemics on a network exhibiting complex temporal dynamics.

Main Results:

  • Successfully detected a small set of informative collective observables from detailed epidemic dynamics.
  • Demonstrated the utility of DMAPS in simplifying the analysis of epidemic spread on evolving networks.

Conclusions:

  • Diffusion Maps offer a promising data-driven approach to identifying essential network statistics for epidemic modeling.
  • This method can aid in developing reduced, accurate macroscopic models of epidemics on adaptive networks.