Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Introduction to Epidemiology

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

Causality in Epidemiology

Causality in Epidemiology

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Investigation of Disease Outbreaks

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

You might also read

Related Articles

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

Sort by

Same author

Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.

Scientific data·2026

Same author

Assessing mosquito dynamics and dengue transmission in Foz do Iguaçu, Brazil through an enhanced temperature-dependent mathematical model.

PloS one·2025

Same author

Large-scale epidemiological modelling: scanning for mosquito-borne diseases spatio-temporal patterns in Brazil.

Royal Society open science·2025

Same author

Epidemic models and their use: Comment on "Mathematical models for dengue fever epidemiology: A 10-year systematic review" by Aguiar et al.

Physics of life reviews·2023

Same author

Fast expansion of dengue in Brazil.

Lancet regional health. Americas·2023

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Epigrass: a tool to study disease spread in complex networks.

Flávio C Coelho¹, Oswaldo G Cruz, Cláudia T Codeço

¹Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.

Source Code for Biology and Medicine

|February 28, 2008

Summary

Epigrass software simplifies creating and simulating network epidemic models. This tool aids in analyzing disease spread, crucial for public health interventions and understanding epidemic dynamics.

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jul 7, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Epidemiology
Computational Biology
Network Science

Background:

Constructing complex spatial simulation models for network epidemiology is data-intensive and time-consuming.
Parameterizing models requires processing large, geo-referenced databases.
Computational tools are essential for efficient model construction, simulation, and analysis.

Purpose of the Study:

Introduce Epigrass, a novel simulation software designed for network-epidemic models.
Automate the design, simulation, and analysis of epidemiological models.
Facilitate the inclusion of diverse node behaviors within network models.

Main Methods:

Development of the Epigrass simulation software.
Application of Epigrass to model disease spread in a Brazilian bus-transportation network.
Utilizing network epidemiology principles for spatial simulation.

Main Results:

A network epidemiological model demonstrated disease spread through a Brazilian bus network.
The study highlighted the critical impact of the epidemic's starting point topology on control and prevention.
Epigrass successfully facilitated the construction and simulation of the complex network model.

Conclusions:

Epigrass significantly streamlines the creation, simulation, and analysis of intricate network models.
Standard GIS output formats from Epigrass enable advanced post-processing and analysis.
The software supports diverse node behaviors and complex epidemiological scenarios.