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

Pareto Chart00:52

Pareto Chart

7.3K
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...
7.3K
Interpreting Run Charts01:25

Interpreting Run Charts

2.8K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
2.8K
Pie Chart01:04

Pie Chart

15.0K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.0K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

253
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:
253
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

600
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:
600
Survival Curves01:18

Survival Curves

371
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
371

You might also read

Related Articles

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

Sort by
Same author

A Scoping Review of Pediatric Kienböck Disease: What Do We Know?

Journal of hand surgery global online·2026
Same author

Quality as an organizational strategy: building a system of improvement.

Frontiers in health services·2026
Same author

Early Impacts of a Medicaid Value-Based Payment Policy on Quality of Care in a Large Population with Serious Mental Illness.

The journal of mental health policy and economics·2026
Same author

Factors Associated with Primary Care, Behavioral Health, and Emergency Department Utilization Among Men with Opioid Use Disorder and Criminal-Legal Involvement: A Cross-Sectional Study.

Research square·2026
Same author

Design and implementation of an AAPM volunteer database for advancing global medical physics initiatives.

Journal of applied clinical medical physics·2026
Same author

A randomized controlled trial of an online group-based internal family systems treatment for posttraumatic stress disorder: The Program for Alleviating and Resolving Trauma and Stress (PARTS) study.

Psychological trauma : theory, research, practice and policy·2026

Related Experiment Video

Updated: Oct 11, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.3K

A hybrid Shewhart chart for visualizing and learning from epidemic data.

Gareth Parry1, Lloyd P Provost2, Shannon M Provost3

  • 1Department of Plastic and Oral Surgery, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA.

International Journal for Quality in Health Care : Journal of the International Society for Quality in Health Care
|December 5, 2021
PubMed
Summary
This summary is machine-generated.

A new hybrid Shewhart chart helps visualize COVID-19 trends, enabling faster detection of epidemic changes. This tool aids decision-makers in implementing timely public health strategies for better outcomes.

Keywords:
Shewhart control chartcovid-19 pandemicstatistical process controlstatistical public reporting of healthcare data

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

337
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.6K

Related Experiment Videos

Last Updated: Oct 11, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.3K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

337
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.6K

Area of Science:

  • Epidemiology and Public Health
  • Statistical Process Control
  • Infectious Disease Dynamics

Background:

  • Daily COVID-19 data reveals distinct epidemic phases: growth, plateau, and decline.
  • Effective decision-making requires rapid detection of shifts in epidemic measures.
  • Timely interventions are crucial for managing pandemic trajectories.

Purpose of the Study:

  • To develop a hybrid Shewhart chart for visualizing and analyzing COVID-19 epidemic measures.
  • To enable real-time learning from day-to-day variations in epidemic data.
  • To support informed public health policy and local decision-making.

Main Methods:

  • A hybrid Shewhart chart was designed, integrating C-chart, I-chart, and log-regression slope.
  • The chart characterizes four epidemic epochs: pre-exponential growth, exponential growth, plateau/descent, and stability.
  • International data on cases, hospitalizations, and deaths were used for testing, with expert guidance.

Main Results:

  • The hybrid chart effectively and rapidly signaled all four epidemic epochs across diverse datasets.
  • In the UK, a COVID-19 deaths surge was detected 44 days before lockdown announcement.
  • In California and Ireland, the chart identified case increases and subsequent declines following policy interventions.

Conclusions:

  • The hybrid Shewhart chart provides a valuable tool for rapid, data-driven learning during pandemics.
  • Subject-matter expert utilization enhances the chart's ability to guide actionable policy.
  • Early detection via the chart can lead to more timely and effective public health interventions.