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

126
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:
126
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

364
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:
364
Types of Skewness01:09

Types of Skewness

11.6K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
11.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

128
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
128
Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K

You might also read

Related Articles

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

Sort by
Same author

Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models.

JMIR mental health·2026
Same author

Gender innovation in the scientific evidence on interventions to reduce health inequalities in Europe: an umbrella review.

International journal for equity in health·2026
Same author

Large reasoning models are autonomous jailbreak agents.

Nature communications·2026
Same author

Fostering nature-based solutions and circular approaches in biogas purification: validation of digestate centrate nitrified by intensified multi-stage constructed wetlands as electron acceptor in anoxic biodesulphurisation.

Bioresource technology·2025
Same author

Service providers' perspectives and reproductive (in)justice among Roma women: a qualitative study in Spain.

Sexual and reproductive health matters·2024
Same author

What is beautiful is still good: the attractiveness halo effect in the era of beauty filters.

Royal Society open science·2024

Related Experiment Video

Updated: Jun 29, 2025

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

1.6K

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.

Kaylin Bolt1, Diana Gil-González2,3, Nuria Oliver4

  • 1Health Sciences Division (Assessment, Policy Development, and Evaluation Unit), Public Health - Seattle & King County, Seattle, WA, United States.

Frontiers in Public Health
|April 3, 2024
PubMed
Summary

Non-traditional data sources like mobility and social media data provided rapid insights during the COVID-19 pandemic. Enhanced data governance and infrastructure are recommended to fully integrate these valuable public health tools.

Keywords:
COVID-19data governancedigital healthnon-traditional dataprecision public health

More Related Videos

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
09:03

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic

Published on: November 7, 2020

4.8K
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

1.8K

Related Experiment Videos

Last Updated: Jun 29, 2025

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

1.6K
Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
09:03

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic

Published on: November 7, 2020

4.8K
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

1.8K

Area of Science:

  • Public Health Surveillance
  • Data Science Applications
  • Epidemiology

Background:

  • The COVID-19 pandemic highlighted the need for diverse data sources beyond traditional methods.
  • Non-traditional data, including mobility, social media, and participatory surveillance, offer potential but also present challenges.
  • Concerns include data biases, representativity, informed consent, and security vulnerabilities associated with non-traditional data.

Purpose of the Study:

  • To explore the successes, challenges, and recommendations for using non-traditional data during the COVID-19 pandemic in Spain and Italy.
  • To identify opportunities for improving the utility and uptake of non-traditional data in public health.
  • To provide guidance for optimizing the use of non-traditional data in public health systems.

Main Methods:

  • Qualitative semi-structured interviews were conducted with experts in AI, data science, epidemiology, and policy.
  • Interviews focused on barriers, facilitators, and opportunities for utilizing non-traditional data.
  • Data were transcribed, coded, and analyzed using the framework analysis method.

Main Results:

  • Non-traditional data proved valuable for rapid insights and filling gaps when traditional data was delayed.
  • Challenges included unreliable access, data quality issues, and lack of demographic/geographic information.
  • Recommendations included prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations.

Conclusions:

  • Non-traditional data demonstrated utility during the pandemic, but enhancements are needed for greater impact.
  • Robust data governance frameworks are essential for guiding the use of non-traditional data in public health.
  • Sustained collaborations and investments in data infrastructure are crucial for integrating non-traditional data into public health systems.