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 Experiment Videos

A space-time permutation scan statistic for disease outbreak detection.

Martin Kulldorff1, Richard Heffernan, Jessica Hartman

  • 1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA. martin_kulldorff@hms.harvard.edu

Plos Medicine
|February 19, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Asyndromic surveillance of New York City emergency department diagnoses with the tree-temporal scan statistic.

Journal of public health research·2026
Same author

Innovative use of self-controlled methods for the evaluation of waning effectiveness of the COVID-19 monovalent third dose: comparison with a test-negative design.

Vaccine·2025
Same author

Applying prospective tree-temporal scan statistics to genomic surveillance data to detect emerging SARS-CoV-2 variants and salmonellosis clusters in New York City.

International journal of epidemiology·2025
Same author

Latent Archetypes of the Spatial Patterns of Cancer.

Statistics in medicine·2024
Same author

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

JMIR public health and surveillance·2024
Same author

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

NEJM evidence·2024
Same journal

Maternal congenital heart disease and risk of child developmental vulnerability in early school age: A population-based cohort study.

PLoS medicine·2026
Same journal

Accelerometry-measured prolonged and interrupted sedentary behavior and cancer incidence and mortality: A cohort study of 91,292 UK Biobank participants.

PLoS medicine·2026
Same journal

Precision oncology's translation gap-Can molecular tumor boards bridge it?

PLoS medicine·2026
Same journal

Improving suicide prevention in men.

PLoS medicine·2026
Same journal

Multilevel onsite training and mentorship model to accelerate early childhood cancer diagnosis in Northwest Ethiopia: A quasi-experimental mixed method study.

PLoS medicine·2026
Same journal

The well-worn path from armed conflict to measles resurgence.

PLoS medicine·2026
See all related articles

A new space-time scan statistic enables early disease outbreak detection using only case numbers. This method aids health departments in surveillance without needing population data, improving public health responses.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Surveillance

Background:

  • Early detection of disease outbreaks is crucial for minimizing morbidity and mortality.
  • Current surveillance systems often lack population-at-risk data.
  • National and local health departments require effective surveillance tools.

Purpose of the Study:

  • To introduce a novel prospective space-time permutation scan statistic for early disease outbreak detection.
  • To develop a method that does not require population-at-risk data, relying solely on case numbers.
  • To create a flexible statistical tool adaptable to various outbreak scenarios.

Main Methods:

  • A prospective space-time permutation scan statistic was developed.
  • The method analyzes only case numbers, making no assumptions about outbreak specifics.

Related Experiment Videos

  • It incorporates adjustments for natural spatial and temporal variations.
  • Evaluation involved daily hospital emergency department visit data in New York City.

    • Main Results:

    • The space-time permutation scan statistic successfully identified potential disease outbreaks.
    • Four of the five strongest signals indicated likely precursors to citywide outbreaks (rotavirus, norovirus, influenza).
    • The method demonstrated a modest number of false positive signals.

    Conclusions:

    • The space-time permutation scan statistic shows promise as a valuable tool for early disease detection.
    • Its effectiveness may be enhanced with longer study durations and broader geographical application.
    • This method can significantly support local and national health departments in establishing robust surveillance systems.