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

232
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:
232
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

576
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:
576
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

196
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...
196
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Censoring Survival Data01:09

Censoring Survival Data

264
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...
264
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

287
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
287

You might also read

Related Articles

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

Sort by
Same author

Maternal ambient air pollution exposure and risk of stillbirth in Georgia, USA.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

Associations Between Acute Heat Exposure and Hospitalization for Takotsubo Syndrome in the State of California, 2006 to 2019.

Journal of the American Heart Association·2026
Same author

Data fusion in air pollution exposure assessment: Methods, applications, and future directions.

Journal of the Air & Waste Management Association (1995)·2026
Same author

Challenges in defining severe influenza with implications for measuring and communicating influenza vaccine effects.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Effects of age and birth cohort on influenza A virus subtype-specific hospitalization rates, United States 2010-2025.

The Journal of infectious diseases·2026
Same author

Community-level influenza activity modifies the association between ambient air pollution and acute respiratory emergency visits in six U.S. Cities.

Scientific reports·2026

Related Experiment Video

Updated: Sep 26, 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

10.8K

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model:

Alexia Couture1, A Danielle Iuliano1,2, Howard H Chang3

  • 1Centers for Disease Control and Prevention, Atlanta, GA, United States.

JMIR Public Health and Surveillance
|April 22, 2022
PubMed
Summary

Estimating COVID-19 hospitalizations using surveillance data provides a sustainable method for tracking the disease burden. This approach offers a flexible framework for long-term monitoring of coronavirus disease 2019.

Keywords:
BayesianCOVID-19COVID-NETSARS-CoV-2United Statesdataestimationextrapolationframeworkhierarchicalhospitalhospitalizationmodelmodelingmonitoringnovelpredictionratesurveillance

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

322
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

7.2K

Related Experiment Videos

Last Updated: Sep 26, 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

10.8K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

322
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

7.2K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • COVID-19 is a nationally notifiable disease in the US, requiring state-level reporting to the CDC.
  • Long-term, facility-level reporting of every COVID-19 case may be infeasible.
  • Sustainable estimation methods using sentinel surveillance are increasingly important for tracking disease burden.

Purpose of the Study:

  • To develop a method for estimating monthly COVID-19 hospitalization rates.
  • To create a long-term solution leveraging existing surveillance data.

Main Methods:

  • Utilized data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) from May 2020 to April 2021.
  • Employed a Bayesian hierarchical model for extrapolation across 50 states and 6 age groups.
  • Incorporated covariate selection using LASSO and spike and slab methods, with validation checks.

Main Results:

  • Estimated 3,583,100 COVID-19 hospitalizations in the US, with a cumulative incidence of 1093.9 per 100,000 population.
  • Highest cumulative incidence observed in individuals aged ≥85 years.
  • Monthly hospitalization rates peaked in December, with significant state-level variations in timing and magnitude.

Conclusions:

  • The novel approach offers a sustainable method for estimating COVID-19 hospitalizations.
  • Provides a flexible framework for ongoing disease burden monitoring.
  • Leverages established surveillance data for robust public health insights.