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

171
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:
171
Causality in Epidemiology01:21

Causality in Epidemiology

699
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...
699
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

493
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:
493
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

550
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
550
Censoring Survival Data01:09

Censoring Survival Data

200
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...
200

You might also read

Related Articles

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

Sort by
Same author

Mathematical modeling of sequential Dengue-Zika infections: dynamic insights into antibody-dependent enhancement and neutralization effects.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Aug 27, 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

10.7K

COVID-19 outbreak: a predictive mathematical study incorporating shedding effect.

Anuraj Singh1, Preeti Deolia1

  • 1ABV-Indian Institute of Information Technology and Management, Gwalior, M.P. India.

Journal of Applied Mathematics & Computing
|September 26, 2022
PubMed
Summary

This study introduces a modified SEIR model to analyze COVID-19 transmission, revealing how pathogen shedding impacts susceptible populations and disease spread. The research identifies critical shedding thresholds for disease control.

Keywords:
Backward bifurcationDisease thresholdEpidemiological modelShedding effect

More Related Videos

Oral Bacterial Infection and Shedding in Drosophila melanogaster
09:32

Oral Bacterial Infection and Shedding in Drosophila melanogaster

Published on: May 31, 2018

11.8K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.3K

Related Experiment Videos

Last Updated: Aug 27, 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

10.7K
Oral Bacterial Infection and Shedding in Drosophila melanogaster
09:32

Oral Bacterial Infection and Shedding in Drosophila melanogaster

Published on: May 31, 2018

11.8K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.3K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • COVID-19 transmission dynamics are complex, influenced by pathogen shedding into the environment.
  • Understanding the impact of shedding on susceptible populations is crucial for effective control strategies.

Purpose of the Study:

  • To propose and analyze a modified SEIR epidemic model incorporating pathogen shedding.
  • To investigate the direct impact of virus shedding on susceptible individuals and disease transmission.
  • To compute critical shedding thresholds and assess their significance in mitigating COVID-19.

Main Methods:

  • Development of a modified Susceptible-Exposed-Infectious-Recovered (SEIR) model.
  • Utilizing the next-generation matrix method to calculate the basic reproduction number (R0), incorporating shedding as a new infection source.
  • Application of bifurcation theory and the center manifold theorem to analyze system stability and bifurcations.
  • Computation of sensitivity indices for model parameters.

Main Results:

  • The model demonstrates that pathogen shedding significantly impacts susceptible populations.
  • Critical shedding parameter thresholds were computed, indicating their importance in disease reduction.
  • The basic reproduction number (R0) was determined, with shedding considered a novel infection pathway.
  • Analysis confirmed that the endemic equilibrium is globally asymptotically stable under specific conditions (R0 > 1).
  • The system exhibits backward bifurcation at a critical R0 value, suggesting complex transmission dynamics.

Conclusions:

  • The modified SEIR model provides valuable insights into COVID-19 transmission, particularly the role of pathogen shedding.
  • Shedding parameters are significant factors influencing disease spread and can be targeted for control.
  • The study highlights the importance of considering shedding in epidemic modeling for accurate predictions and interventions.