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

170
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:
170
Causality in Epidemiology01:21

Causality in Epidemiology

649
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...
649
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

112
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...
112
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

129
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
129
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

1.7K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
1.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

79
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...
79

You might also read

Related Articles

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

Sort by
Same journal

10-Year Change in the Laboratory-Based Prevalence of Chronic Kidney Disease in Patients from a Brazilian Cardiologic Center.

Epidemiologia (Basel, Switzerland)·2026
Same journal

Interpretation of Epidemiological Studies on the Relationship Between Mobile Phone Use and Cancer.

Epidemiologia (Basel, Switzerland)·2026
Same journal

Geospatial Visualisation of Distance to General Practitioner Facilities with Population Density Patterns in the United Kingdom.

Epidemiologia (Basel, Switzerland)·2026
Same journal

Excess Weight and Dyslipidemia in Seri (Comcáac) Indigenous Children: A Cross-Sectional Study of Prevalences and Associated Factors.

Epidemiologia (Basel, Switzerland)·2026
Same journal

Quantifying Epidemiological Risk Transitions of COVID-19 in the Brazilian State of Ceará (2020-2023): A Generalized Linear Modeling Approach.

Epidemiologia (Basel, Switzerland)·2026
Same journal

Prevalence and Determinants of Uncontrolled Hypertension Among Treated Adults in a Rural Primary Health Care Facility in South Africa: A Cross-Sectional Study.

Epidemiologia (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 20, 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

2.6K

On Deterministic and Stochastic Multiple Pathogen Epidemic Models.

Fernando Vadillo1

  • 1Department of Mathematics, University of the Basque Country (UPV/EHU), Apdo 644, 48080 Bilbao, Spain.

Epidemiologia (Basel, Switzerland)
|November 23, 2022
PubMed
Summary

This study analyzes a two-pathogen epidemic model. Stochastic models significantly differ from deterministic ones in predicting disease coexistence and extinction outcomes.

Keywords:
epidemic modelsfinite element methodpersistence timestochastic differential equation

More Related Videos

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

36.4K
An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.0K

Related Experiment Videos

Last Updated: Aug 20, 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

2.6K
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

36.4K
An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.0K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Science

Background:

  • Understanding pathogen coexistence is crucial for disease management.
  • Stochastic effects can significantly alter epidemic dynamics compared to deterministic models.

Purpose of the Study:

  • To numerically analyze the coexistence of two pathogens using a stochastic epidemic model.
  • To compare the predictive power of stochastic versus deterministic epidemic models.

Main Methods:

  • Numerical computation of the mean persistence time (expectation time until extinction).
  • Solving a stationary partial differential equation using the finite element method (FEM) implemented in FreeFem++.
  • Utilizing a backward Kolmogorov equation framework.

Main Results:

  • The finite element method was successfully implemented to solve the complex partial differential equation.
  • Significant discrepancies were observed between stochastic and deterministic models in predicting pathogen coexistence.
  • Stochastic models provided different outcomes regarding the extinction of one pathogen compared to deterministic approaches.

Conclusions:

  • Stochastic epidemic models offer a more nuanced understanding of pathogen coexistence and extinction dynamics.
  • Deterministic models may provide misleading predictions in complex epidemiological scenarios with multiple pathogens.
  • Further research is needed to elucidate the underlying reasons for the observed differences between stochastic and deterministic model predictions.