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

Prevalence and Incidence01:08

Prevalence and Incidence

1.4K
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
1.4K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

480
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
480
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

799
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:
799
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

756
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
756
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

393
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...
393
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

414
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:
414

You might also read

Related Articles

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

Sort by
Same author

Smartwatch Based Atrial Fibrillation Detection from Photoplethysmography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Glycan Profiles of gp120 Protein Vaccines from Four Major HIV-1 Subtypes Produced from Different Host Cell Lines under Non-GMP or GMP Conditions.

Journal of virology·2020
Same author

Effects of a high-fat diet on intracellular calcium (Ca2+) handling and cardiac remodeling in Wistar rats without hyperlipidemia.

Ultrastructural pathology·2020
Same author

A strategy for iron oxide nanoparticles to adhere to the neuronal membrane in the substantia nigra of mice.

Journal of materials chemistry. B·2020
Same author

Ultrasound/Optical Dual-Modality Imaging for Evaluation of Vulnerable Atherosclerotic Plaques with Osteopontin Targeted Nanoparticles.

Macromolecular bioscience·2019
Same author

MicroRNA-23a suppresses the apoptosis of inflammatory macrophages and foam cells in atherogenesis by targeting HSP90.

Gene·2019

Related Experiment Video

Updated: Dec 19, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Multichart Schemes for Detecting Changes in Disease Incidence.

Gideon Mensah Engmann1,2, Dong Han1

  • 1School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China.

Computational and Mathematical Methods in Medicine
|June 9, 2020
PubMed
Summary

The cumulative sum (CUSUM) multi-chart scheme is the fastest method for detecting changes in disease incidence compared to Exponentially Weighted Moving Average (EWMA) and EWMA-CUSUM schemes. This finding aids in timely public health interventions.

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.5K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

420

Related Experiment Videos

Last Updated: Dec 19, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.5K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

420

Area of Science:

  • Epidemiology
  • Statistical Process Control
  • Public Health Surveillance

Background:

  • Effective disease outbreak detection is crucial for public health.
  • Existing methods for monitoring disease incidence include likelihood-based approaches and control charts like Shewhart, CUSUM, and EWMA.
  • Multi-chart schemes offer potential advantages in speed and computational efficiency.

Purpose of the Study:

  • To evaluate and compare the performance of CUSUM, EWMA, and EWMA-CUSUM multi-chart schemes for detecting changes in disease incidence.
  • To determine which multi-chart scheme offers the fastest detection of shifts in disease rates.

Main Methods:

  • Implementation and simulation of CUSUM, EWMA, and EWMA-CUSUM multi-chart schemes.
  • Comparative analysis of detection speed for shifts in the rate parameter using simulation data.
  • Application of the most efficient scheme to real-world health data for validation.

Main Results:

  • Simulation results indicate that the multi-CUSUM chart demonstrates superior speed in detecting shifts in the rate parameter compared to EWMA and EWMA-CUSUM multi-charts.
  • The multi-chart approach, in general, is computationally efficient.
  • Real health data analysis confirmed the practical efficiency of the evaluated schemes.

Conclusions:

  • The CUSUM multi-chart scheme is recommended for its enhanced speed in detecting changes in disease incidence.
  • Multi-chart schemes provide a computationally efficient and effective tool for disease surveillance.
  • These findings can improve the timeliness of responses to disease outbreaks.