Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Empirical Method to Interpret Standard Deviation

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...

Models of Health Promotion and Illness Prevention II

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...

Models of Health Promotion and Illness Prevention I

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...

Interpreting R Charts

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...

Nursing Implementation

Nursing Implementation

Implementation is the execution of the nursing care plan developed during the planning phase.
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.

Interpreting Run Charts

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...

You might also read

Related Articles

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

Sort by

Same author

Local Influenza Forecasts Outperform State-Level Forecasts in the United States.

medRxiv : the preprint server for health sciences·2026

Same author

Beyond forecast leaderboards: Measuring individual model importance based on contribution to ensemble accuracy.

International journal of forecasting·2026

Same author

Retrospective evaluation of trained and untrained probabilistic ensemble forecasts for influenza hospital admissions - United States, 2022-2025.

Infectious Disease Modelling·2026

Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

medRxiv : the preprint server for health sciences·2026

Same author

Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus.

medRxiv : the preprint server for health sciences·2026

Same author

An evaluation of a Bayesian method to track outbreaks of known and novel influenza-like illnesses.

Scientific reports·2026

Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026

Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026

Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026

Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026

Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026

Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE

Published on: March 1, 2024

Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R.

Li Shandross¹, Emily Howerton², Lucie Contamin³

¹Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts, USA.

Statistics in Medicine

|January 22, 2026

Summary

This summary is machine-generated.

Multi-model ensembles improve public health forecasts by combining predictions. The new hubEnsembles package offers a flexible framework and tutorial for practical application in infectious disease outbreak forecasting.

Keywords:

aggregation forecast multiple models prediction

More Related Videos

Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock

Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock

Published on: May 6, 2010

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

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: Jan 23, 2026

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE

Published on: March 1, 2024

Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock

Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock

Published on: May 6, 2010

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

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:

Computational epidemiology
Statistical modeling
Public health informatics

Background:

Multi-model ensembles are widely used in forecasting for performance benefits.
Their application is growing in public health for infectious disease outbreak prediction.
Challenges include interpreting diverse methods and lack of standardized software.

Purpose of the Study:

To introduce the statistical foundations of probabilistic forecasting and multi-model ensembles.
To present the hubEnsembles package as a flexible software framework.
To provide a tutorial and case study for practical ensemble generation.

Main Methods:

Introduction to statistical foundations of probabilistic forecasting.
Development and presentation of the hubEnsembles software package.
Tutorial and case study using real-world data from the FluSight Forecast Hub.

Main Results:

Demonstrated the utility of multi-model ensembles for improved outbreak forecasting.
Introduced a flexible framework (hubEnsembles) for practical ensemble generation.
Provided a reproducible case study for applying ensemble methods.

Conclusions:

Multi-model ensembles offer enhanced accuracy and reliability in public health forecasts.
The hubEnsembles package addresses practical challenges in generating and interpreting ensemble predictions.
Standardized tools are crucial for advancing the application of ensemble methods in epidemiology.