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:
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

You might also read

Related Articles

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

Sort by
Same author

Deployment to Karshi-Khanabad Air Base, Uzbekistan between 2001 and 2005 and subsequent risk of specific cancers among US service members.

Journal of the National Cancer Institute·2026
Same author

Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.

JMIR medical informatics·2022
Same author

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data.

JMIR medical informatics·2021
Same author

Equine syndromic surveillance in Colorado using veterinary laboratory testing order data.

PloS one·2019
Same author

A Practitioner-Driven Research Agenda for Syndromic Surveillance.

Public health reports (Washington, D.C. : 1974)·2017
Same author

Evolution of Public Health Surveillance: Status and Recommendations.

American journal of public health·2017

Related Experiment Video

Updated: Jun 20, 2026

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device
06:51

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Published on: July 29, 2016

Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

Yevgeniy Elbert1, Howard S Burkom

  • 1Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA. yevgeniy.elbert@jhuapl.edu

Statistics in Medicine
|September 3, 2009
PubMed
Summary

This study enhances Holt-Winters forecasts for syndromic time series, improving biosurveillance alerting. The new algorithm shows comparable or superior sensitivity and timeliness to traditional methods.

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jun 20, 2026

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device
06:51

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Published on: July 29, 2016

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Epidemiology
  • Biostatistics
  • Time Series Analysis

Background:

  • Syndromic surveillance systems rely on timely detection of health events.
  • Traditional methods for analyzing time series data in biosurveillance have limitations.
  • Robust forecasting is crucial for effective public health monitoring.

Purpose of the Study:

  • To advance robust predictions using Holt-Winters forecasts for syndromic time series.
  • To introduce a control-chart detection approach based on these forecasts.
  • To compare the performance of a novel Holt-Winters-based alerting algorithm against traditional methods.

Main Methods:

  • Utilized three collections of time series data (authentic and simulated).
  • Developed an alerting algorithm using Holt-Winters-generalized smoothing.
  • Compared the algorithm against simple control-chart adaptations and regression modeling methods.
  • Evaluated methods using measures of forecast agreement, signal sensitivity, and time-to-detect.

Main Results:

  • The Holt-Winters-based algorithm demonstrated comparable or superior sensitivity.
  • The timeliness of detection using the new algorithm was also comparable or superior.
  • Practical rules for initialization and parameterization of biosurveillance time series were established.

Conclusions:

  • The proposed Holt-Winters-based alerting algorithm is a viable and effective tool for syndromic surveillance.
  • This approach offers improvements in both accuracy and speed for detecting public health signals.
  • The findings support the prospective application of this method to daily syndromic time series data.