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

One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.4K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.4K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

616
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
616
Sampling Plans01:23

Sampling Plans

228
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
228
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

You might also read

Related Articles

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

Sort by
Same journal

Global dynamics of a spatially heterogeneous diffusive two-strain epidemic model with varying total population.

Journal of mathematical biology·2026
Same journal

Exploring the evolution of maturation time via strong competition model with stage structure.

Journal of mathematical biology·2026
Same journal

Dynamical analysis of a stochastic dual-strain infectious model with hospital beds and logarithmic Ornstein-Uhlenbeck process.

Journal of mathematical biology·2026
Same journal

A reaction telegraph model reveals synergy between motility strategies in Myxococcus xanthus predation.

Journal of mathematical biology·2026
Same journal

Numerical modeling of fluid exchange between a collecting lymphatic vessel and the surrounding tissue.

Journal of mathematical biology·2026
Same journal

A perception-memory PDE framework for seasonal migration dynamics.

Journal of mathematical biology·2026

Related Experiment Video

Updated: Aug 6, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Final size and partial distance estimate for a two-group SEIRD model.

Alison M V D L Melo1, Matheus C Santos2

  • 1Universidade Federal do Vale do São Francisco - UNIVASF, Petrolina, 56304-917, Brazil.

Journal of Mathematical Biology
|March 18, 2023
PubMed
Summary

This study introduces a SEIRD epidemic model for two interacting groups, estimating approximation errors and final epidemic sizes. The findings are applied to COVID-19 spread in New York and Brazil.

Keywords:
Distance of solutionsEpidemic mathematical modelFinal sizeLatency period

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Related Experiment Videos

Last Updated: Aug 6, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Computational Science

Background:

  • Understanding disease spread in heterogeneous populations is crucial.
  • Asymmetric interactions between population groups complicate epidemic modeling.
  • Accurate estimation of approximation errors is vital for model reliability.

Purpose of the Study:

  • To develop and analyze a two-group SEIRD epidemic model with asymmetric interactions.
  • To estimate the approximation error for the second group using known errors for the first group.
  • To investigate the final epidemic size for each population group.

Main Methods:

  • Utilizing a SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased) compartmental model.
  • Developing an approximate solution for the two-group model.
  • Implementing error estimation techniques based on known approximation errors.
  • Analyzing epidemic dynamics and final sizes for distinct population groups.

Main Results:

  • A method for estimating approximation errors in a two-group SEIRD model was established.
  • The final epidemic size was determined for each population group under asymmetric interactions.
  • The model's applicability was demonstrated using initial COVID-19 data from New York County and Brazil.

Conclusions:

  • The proposed method effectively estimates approximation errors in complex epidemic models.
  • The study provides insights into disease dynamics and final sizes in multi-group populations.
  • This approach offers a valuable tool for analyzing and predicting infectious disease spread.
  • The findings are relevant for public health strategies during pandemics.