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

396
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:
396
Causality in Epidemiology01:21

Causality in Epidemiology

1.3K
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...
1.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.1K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

775
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:
775
Pareto Chart00:52

Pareto Chart

7.5K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.5K
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

374
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
374

You might also read

Related Articles

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

Sort by
Same author

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review.

Bulletin of earthquake engineering·2024
Same author

Nonlinear Dynamic Response of Nanocomposite Microbeams Array for Multiple Mass Sensing.

Nanomaterials (Basel, Switzerland)·2023
Same author

Modified SIQR model for the COVID-19 outbreak in several countries.

Mathematical methods in the applied sciences·2022
Same author

Fractional-Order Sensing and Control: Embedding the Nonlinear Dynamics of Robot Manipulators into the Multidimensional Scaling Method.

Sensors (Basel, Switzerland)·2021
Same author

Advances in the computational analysis of SARS-COV2 genome.

Nonlinear dynamics·2021
Same author

Uniform Manifold Approximation and Projection Analysis of Soccer Players.

Entropy (Basel, Switzerland)·2021

Related Experiment Video

Updated: Dec 9, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.2K

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.

Giuseppe Quaranta1, Giovanni Formica2, J Tenreiro Machado3

  • 1Department of Structural and Geotechnical Engineering, Sapienza University of Rome, via Eudossiana 18, Rome, Italy.

Nonlinear Dynamics
|September 9, 2020
PubMed
Summary

This study analyzes the COVID-19 pandemic

Keywords:
COVID-19Compartmental modelComputational intelligenceLogistic regressionNonlinear infection dynamicsParametric identification

More Related Videos

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K
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

1.7K

Related Experiment Videos

Last Updated: Dec 9, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.2K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K
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

1.7K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Spatial Analysis

Background:

  • The COVID-19 pandemic's initial outbreak in Italy occurred in Lombardy.
  • The virus spread across northern and central Italy with varied temporal and spatial dynamics.

Purpose of the Study:

  • To conduct a multi-scale territorial analysis of the COVID-19 pandemic in Italy.
  • To understand the nonlinear and spatially nonuniform epidemic spreading patterns.

Main Methods:

  • Logistic regression to model total positive cases.
  • Enhanced SIR-type model with differential evolution for territorial dynamics.
  • Hierarchical clustering and multidimensional analysis for geographical comparisons.

Main Results:

  • Identified distinct temporal and spatial patterns of COVID-19 spread across Italian regions.
  • Revealed similarities and dissimilarities in epidemic developments using clustering and multidimensional analysis.
  • Parametric identification and data-driven analyses provided insights into epidemic dynamics.

Conclusions:

  • The study offers a comprehensive understanding of Italy's COVID-19 epidemic spreading.
  • Multi-scale analysis is crucial for deciphering complex, geographically varied disease transmission.