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

Mechanistic Models: Compartment Models in Individual and Population Analysis

81
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...
81
Prevalence and Incidence01:08

Prevalence and Incidence

741
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
741
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

494
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:
494
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

673
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...
673
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100

You might also read

Related Articles

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

Sort by
Same author

The attribution of human health outcomes to climate change: a transdisciplinary guidance document.

Climatic change·2025
Same author

An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.

Royal Society open science·2025
Same author

Demographic effects of aggregation in the presence of a component Allee effect.

Journal of the Royal Society, Interface·2024
Same author

[Covid-19 in the Northeast of Brazil: from lockdown to the relaxation of social distancing measures].

Ciencia & saude coletiva·2021
Same author

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.

Nature communications·2021
Same author

COVID-19 in Northeast Brazil: achievements and limitations in the responses of the state governments.

Ciencia & saude coletiva·2020

Related Experiment Video

Updated: Aug 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Estimating the effective reproduction number for heterogeneous models using incidence data.

D C P Jorge1,2, J F Oliveira3, J G V Miranda2

  • 1Instituto de Física Teórica, Universidade Estadual Paulista-UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, Brazil.

Royal Society Open Science
|September 22, 2022
PubMed
Summary

This study introduces a new mathematical method to estimate the reproduction number and generation interval distribution for infectious diseases, even in complex populations. This approach was applied to COVID-19 in Rio de Janeiro, Brazil, to understand disease spread between municipalities.

Keywords:
COVID-19effective reproduction numbermathematical modelsmeta-population models

More Related Videos

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

8.8K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Related Experiment Videos

Last Updated: Aug 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

8.8K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • The effective reproduction number (R) is crucial for tracking epidemics.
  • Estimating R typically relies on the generation interval distribution (g(τ)).
  • Existing methods struggle with heterogeneous populations or disease expression, lacking generalizable methodologies for g(τ).

Purpose of the Study:

  • To develop a general mathematical methodology for deriving explicit expressions for reproduction numbers and generation interval distributions.
  • To provide methods for evaluating these parameters using incidence data.
  • To apply this methodology to understand COVID-19 transmission dynamics in Rio de Janeiro.

Main Methods:

  • Utilized mathematical modeling to derive general expressions for R and g(τ) within and between sub-compartments of compartmental models.
  • Developed specific formulas for calculating R from incidence data.
  • Applied two meta-population models to analyze COVID-19 spread in Rio de Janeiro.

Main Results:

  • Successfully derived explicit expressions for reproduction numbers and generation interval distributions for arbitrary compartmental models.
  • Demonstrated the application of the methodology to COVID-19 data from Rio de Janeiro.
  • Estimated reproduction numbers and quantified inter-municipal disease transmission contributions.

Conclusions:

  • The developed methodology offers a robust framework for analyzing infectious disease dynamics in heterogeneous systems.
  • This approach enhances the understanding of epidemic spread, particularly in complex scenarios like inter-regional transmission.
  • The findings provide valuable insights for public health interventions and disease control strategies.