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

225
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:
225
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

569
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:
569
Causality in Epidemiology01:21

Causality in Epidemiology

951
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...
951
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

193
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...
193
Correlation01:09

Correlation

12.5K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
12.5K
Correlation and Causation01:27

Correlation and Causation

39.7K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
39.7K

You might also read

Related Articles

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

Sort by
Same author

Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study.

Journal of medical Internet research·2026
Same author

Spatial and telehealth accessibility to eating disorder treatment in the United States: evidence from registry and LLM-augmented data.

International journal of health geographics·2026
Same author

The rising threats of global wildland-human interface revealed by a scale-adaptive approach.

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

H3-MOSAIC: multimodal generative AI for semantic place detection from high-frequency GPS on H3 grids in mental health geomatics.

International journal of health geographics·2025
Same author

Cancer incidence data at the ZIP Code Tabulation Area level in the United States interpolated by Monte Carlo simulation with multiple constraints.

Scientific data·2025
Same author

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2024

Related Experiment Video

Updated: Sep 23, 2025

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K

Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data.

Ru Wang1, Lingbo Liu1,2, Hao Wu3

  • 1Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China.

International Journal of Environmental Research and Public Health
|May 14, 2022
PubMed
Summary

Urban spatial factors like population density and mobility significantly influence COVID-19 transmission. Understanding these urban environmental factors is crucial for public health planning and creating safer cities.

Keywords:
COVID-19 transmissionsocial media dataurban elementsurban planning

More Related Videos

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

1.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Related Experiment Videos

Last Updated: Sep 23, 2025

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K
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

1.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Area of Science:

  • Urban planning
  • Public health
  • Epidemiology

Background:

  • COVID-19 presents a global urban public health challenge.
  • The impact of urban spatial factors on disease transmission remains poorly understood.

Purpose of the Study:

  • To investigate the correlation between urban environmental factors and COVID-19 transmission outcomes.
  • To identify key spatial determinants of disease spread in Wuhan.

Main Methods:

  • Utilized geotagged COVID-19 case data from social media during the early pandemic stage.
  • Employed multiple regression analysis to assess spatial factor correlations.

Main Results:

  • Population density, human mobility, and environmental quality showed strong correlations with COVID-19 case aggregation areas.
  • Identified specific urban spatial factors influencing disease transmission patterns.

Conclusions:

  • Spatial factors are critical in shaping COVID-19 transmission dynamics within urban environments.
  • Findings offer valuable insights for urban planners and administrators to enhance city resilience and safety.