Jove
Visualize
Contact Us

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
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

484
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:
484
Causality in Epidemiology01:21

Causality in Epidemiology

684
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...
684
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

747
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
747
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

169
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...
169
Introduction to Epidemiology01:26

Introduction to Epidemiology

917
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,...
917

You might also read

Related Articles

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

Sort by
Same journal

Retraction Note: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model.

Soft computing·2026
Same journal

Retraction Note: A review on quantum computing and deep learning algorithms and their applications.

Soft computing·2026
Same journal

Retraction Note: Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation.

Soft computing·2026
Same journal

Retraction Note: Quantum K-means clustering method for detecting heart disease using quantum circuit approach.

Soft computing·2026
Same journal

Retraction Note: DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection: Nancy Girdhar.

Soft computing·2026
Same journal

Retraction Note: Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN.

Soft computing·2026
See all related articles
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 Experiment Video

Updated: Aug 24, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.2K

Algorithms for Covid-19 outbreak using soft set theory: estimation and application.

Orhan Dalkılıç1, Naime Demirtaş1

  • 1Department of Mathematics, Faculty of Arts and Sciences, Mersin University, Mersin, Turkey.

Soft Computing
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces soft set theory algorithms to analyze Coronavirus disease (COVID-19) symptoms and their regional variations. Findings suggest localized analysis is crucial for effective epidemic management and early COVID-19 testing.

Keywords:
Covid-19Decision makingSoft set

More Related Videos

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
Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
10:53

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration

Published on: March 17, 2023

1.7K

Related Experiment Videos

Last Updated: Aug 24, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.2K
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
Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
10:53

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration

Published on: March 17, 2023

1.7K

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Public Health

Background:

  • Coronavirus disease (COVID-19), caused by SARS-COV2, is a significant global health concern.
  • Understanding the diverse and evolving symptoms of COVID-19 is critical for effective management.
  • Reported symptoms range from common fever, cough, and shortness of breath to severe pneumonia, respiratory failure, kidney failure, and death, with asymptomatic cases also noted.

Purpose of the Study:

  • To analyze the relationships and dominance of COVID-19 symptoms using soft set theory.
  • To investigate the necessity of regional evaluation for understanding COVID-19 symptom presentation.
  • To identify key indicators for early COVID-19 testing.

Main Methods:

  • Development of two distinct algorithms based on soft set theory.
  • Algorithm 1: Analysis of interrelationships between COVID-19 symptoms.
  • Algorithm 2: Identification of the most dominant COVID-19 symptom.

Main Results:

  • The study's algorithms indicate that regional variations in COVID-19 symptoms are significant.
  • Analysis suggests that localized symptom evaluation is more beneficial for epidemic control than a generalized approach.
  • Consistent patterns were identified regarding the initial symptoms indicative of a need for COVID-19 testing.

Conclusions:

  • Regional analysis of COVID-19 symptoms is essential for effective epidemic management.
  • Soft set theory provides a valuable framework for understanding complex symptomology.
  • Identifying dominant and early symptoms can improve diagnostic strategies and public health responses.