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

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:
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:

You might also read

Related Articles

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

Sort by
Same author

MRI-based perfusion-diffusion habitat analysis for characterizing intratumoral heterogeneity in rectal adenocarcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society·2026
Same author

Combined ipsilateral peak systolic velocity and ASPECTS are associated with early neurological deterioration after endovascular thrombectomy: a prospective cohort study.

BMC neurology·2026
Same author

Causal Discovery in Observational Medical Research: Scoping Review.

JMIR medical informatics·2026
Same author

Mediating role of arterial stiffness in the association between physical activity and cardiovascular disease risk: a prospective cohort study.

Scientific reports·2025
Same author

Single-Cell RNA Sequencing of Thyroid Tissues Reveals Pathogenesis of Graves' Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Primary lung cancer with duodenal metastasis complicated by obstructive jaundice and pancreatitis: a case report.

Frontiers in oncology·2025

Related Experiment Video

Updated: May 21, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Epidemic features affecting the performance of outbreak detection algorithms.

Jie Kuang1, Wei Zhong Yang, Ding Lun Zhou

  • 1Department of Occupational Health, West China School of Public Health, Sichuan University, 17 South Section 3 Renmin Road, Chengdu, Sichuan 610041, China.

BMC Public Health
|June 12, 2012
PubMed
Summary

The moving percentile method (MPM) demonstrates superior performance in detecting infectious disease outbreaks compared to EWMA and CUSUM algorithms. Epidemic features significantly influence detection sensitivity and timeliness, crucial for automated surveillance systems.

Related Experiment Videos

Last Updated: May 21, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Area of Science:

  • Public Health Surveillance
  • Infectious Disease Epidemiology
  • Biostatistics

Background:

  • Automated surveillance systems rely on effective outbreak detection algorithms.
  • Limited research exists on how specific infectious disease epidemic features impact algorithm performance.
  • This study addresses the gap by evaluating algorithm performance across various epidemic characteristics.

Purpose of the Study:

  • To compare the detection performance of three common outbreak detection algorithms: EWMA, CUSUM, and MPM.
  • To analyze how epidemic features (incubation period, baseline counts, outbreak magnitude) affect algorithm sensitivity and timeliness.
  • To provide insights for optimizing automated surveillance practices.

Main Methods:

  • Simulated outbreaks were introduced into China's notifiable infectious disease data (CIDARS).
  • EWMA, CUSUM, and MPM algorithms were applied and compared at a 5% false alarm rate.
  • Multiple linear regression analyzed the relationship between epidemic features and algorithm performance (sensitivity, timeliness).

Main Results:

  • MPM consistently outperformed EWMA and CUSUM in outbreak detection across all simulated scenarios.
  • Epidemic features significantly correlated with both detection sensitivity and timeliness.
  • Short incubation periods, lower baseline counts, and larger outbreak magnitudes influenced detection speed and accuracy.

Conclusions:

  • The moving percentile method (MPM) is recommended as a preferred algorithm for outbreak detection.
  • Automated surveillance systems should account for variations in epidemic features for improved performance.
  • Understanding epidemic characteristics is vital for enhancing the effectiveness of public health surveillance.