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:
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...
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:
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...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

You might also read

Related Articles

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

Sort by
Same author

Packaged intervention targeting oral antimicrobials in Japanese tertiary care outpatient setting: a quasi-experimental study.

JAC-antimicrobial resistance·2026
Same author

Association between food group intakes and metabolic acidosis in patients with non-dialysis-dependent chronic kidney disease.

BMC nephrology·2026
Same author

Registry-based estimation of cardiac event-free survival in congenital heart disease complicated by pulmonary hypertension: A nationwide registry study from Japan.

International journal of cardiology. Congenital heart disease·2026
Same author

Association of dietary diversity measured by the number of dishes with cardiovascular risk factors among Japanese adults: findings from the National Health and Nutrition Survey, 2018-19.

Nutrition journal·2026
Same author

Prediction of Paroxysmal Atrial Fibrillation With Incorporating Genomic Information Into AI-Based ECG Analysis.

JACC. Asia·2026
Same author

Comparison of the Safety and Efficacy between Endovascular Coiling and Surgical Clipping of Posterior Communicating Artery Aneurysms: A 10-year Retrospective Analysis of 851 Aneurysms.

Neurologia medico-chirurgica·2026

Related Experiment Video

Updated: Jun 14, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A space-time scan statistic for detecting emerging outbreaks.

Toshiro Tango1, Kunihiko Takahashi, Kazuaki Kohriyama

  • 1Department of Technology Assessment and Biostatistics, National Institute of Public Health, 3-6 Minami 2 chome Wako Saitama-ken 351-0197, Japan. tango@niph.go.jp

Biometrics
|April 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel space-time scan statistic for improved outbreak detection. The new method accurately identifies localized emerging diseases by comparing observed cases with unconditional expected numbers, enhancing syndromic surveillance systems.

More Related Videos

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

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Related Experiment Videos

Last Updated: Jun 14, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Kulldorff's space-time scan statistic is widely used for outbreak detection but struggles with noncircular clusters due to its reliance on circular spatial windows.
  • Existing flexible space-time scan statistics may not precisely detect localized emerging disease outbreaks as they compare observed cases to conditional expected numbers.

Purpose of the Study:

  • To propose a new space-time scan statistic that improves the timely and accurate detection of localized emerging disease outbreaks.
  • To address limitations of existing methods by incorporating unconditional expected case numbers and time-varying Poisson means.

Main Methods:

  • Developed a novel space-time scan statistic comparing observed cases with unconditional expected cases.
  • Incorporated time-to-time variation of the Poisson mean into the statistical model.
  • Implemented an outbreak model designed for capturing localized emerging disease outbreaks.

Main Results:

  • The proposed method demonstrated improved accuracy in detecting localized emerging disease outbreaks compared to existing methods.
  • The model successfully captured time-to-time variations in disease incidence.
  • Application to primary school absentee data from Kitakyushu-shi, Japan, illustrated the model's effectiveness.

Conclusions:

  • The new space-time scan statistic offers a more accurate and timely approach for detecting localized emerging disease outbreaks.
  • This method enhances the capabilities of syndromic surveillance systems by addressing the limitations of circular cluster detection.
  • The model's ability to account for temporal variations and use unconditional expected values represents a significant advancement in epidemiological surveillance.