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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

475
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:
475
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

490
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:  
490
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.2K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

168
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:
168

You might also read

Related Articles

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

Sort by
Same author

Harmonizing intervention, context, and implementation strategies with Context-Driven Co-Design (CD2).

Implementation science communications·2026
Same author

A Multilevel Analysis of HIV Care Outcomes Across Age, Race, and Housing Among United States Women Veterans.

Healthcare (Basel, Switzerland)·2026
Same author

Demographic and Mental Health Predictors of HIV Care Outcomes Among Women Veterans.

AIDS and behavior·2026
Same author

A qualitative examination of human papillomavirus vaccine access, beliefs, and behaviors among adult males aged 18-35: applying the Health Stigma and Discrimination Framework.

Frontiers in public health·2026
Same author

Barriers and Facilitators to the Implementation of the Diabetes Prevention Program (DPP) in Health Settings: Protocol for a Mixed Methods Systematic Review.

Campbell systematic reviews·2026
Same author

Feasibility study of a behavioral lifestyle intervention for Hispanic/Latino patients with metabolic dysfunction-associated steatotic liver disease.

BMC public health·2026

Related Experiment Video

Updated: Aug 17, 2025

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

A Novel Bayesian Spatial-Temporal Approach to Quantify SARS-CoV-2 Testing Disparities for Small Area Estimation.

Cici Bauer1, Xiaona Li1, Kehe Zhang1

  • 1Cici Bauer, Xiaona Li, and Kehe Zhang are with the Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston. Miryoung Lee, Susan Fisher-Hoch, and Joseph McCormick are with the Department of Epidemiology, Human Genetics and Environmental Science, School of Public Health, The University of Texas Health Science Center at Houston. Esmeralda Guajardo is with the Cameron County Public Health, San Benito, TX. Maria E. Fernandez and Belinda Reininger are with the Department of Health Promotion and Behavior Sciences, School of Public Health, The University of Texas Health Science Center at Houston.

American Journal of Public Health
|December 14, 2022
PubMed
Summary

This study introduces a new Bayesian spatial-temporal method to find and measure severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing gaps in small areas. The approach helps identify communities needing more testing for better COVID-19 response.

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

1.2K
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

Related Experiment Videos

Last Updated: Aug 17, 2025

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
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
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

Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities can impact public health outcomes.
  • Accurate identification of testing deficiencies is crucial for effective resource allocation and intervention planning.
  • Small area estimation methods are needed to understand localized testing patterns.

Purpose of the Study:

  • To develop and validate a novel Bayesian spatial-temporal model for identifying and quantifying SARS-CoV-2 testing disparities.
  • To estimate testing positivity rates (TPR) at the census block group (CBG) level.
  • To pinpoint areas with testing deficiencies and determine the number of additional tests required.

Main Methods:

  • Employed a Bayesian inseparable space-time model to estimate TPR in granular geographic areas (CBGs).
  • Utilized a rank-based approach to compare estimated TPR with testing rates, identifying areas with testing deficits.
  • Applied weekly SARS-CoV-2 infection and testing data from Cameron County, Texas (March 2020 - February 2022).

Main Results:

  • Successfully identified CBGs experiencing significant testing deficiencies.
  • Quantified the number of tests that were needed in underserved areas.
  • Evaluated both short-term and long-term patterns of testing disparities.

Conclusions:

  • The proposed analytical framework provides a tool for understanding SARS-CoV-2 testing disparities in small communities.
  • This approach can inform COVID-19 response planning and intervention strategies to improve testing uptake.
  • Aids policymakers and public health practitioners in targeted resource allocation and goal setting.