Jove
Visualize
Contact Us

Related Concept Videos

Experimental Designs01:16

Experimental Designs

18.5K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
18.5K
Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.7K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.7K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

449
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
449
Hindsight Biases01:12

Hindsight Biases

4.5K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.5K
Survival Tree01:19

Survival Tree

468
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
468

You might also read

Related Articles

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

Sort by
Same author

Adaptive multi-model ensembles for improved epidemic projections and decision support.

medRxiv : the preprint server for health sciences·2026
Same author

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

medRxiv : the preprint server for health sciences·2026
Same author

Global Epidemiology of Post-Transplant Lymphoproliferative Disorder (PTLD) in Hematopoietic Stem Cell Transplantation (HSCT): A Systematic Review and Meta-Analysis of Incidence, Subtypes, Risk Factors, and Beyond.

Journal of clinical medicine·2026
Same author

Hemodialysis Tends to Improve Thyroid Function by Restoring Hormone Levels in ESRD Patients Compared to Non-Dialysis Kidney Disease Patients: A Case-Control Study.

Diseases (Basel, Switzerland)·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

Global epidemiology of hepatitis B virus infection in dialysis patients: a systematic review and meta-analysis of prevalence, immunity, risk factors, and beyond.

International urology and nephrology·2026
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 Video

Updated: Mar 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.

Ehsan Suez1,2, Spencer J Fox1,2,3

  • 1Institute of Bioinformatics, University of Georgia, Athens, GA, USA.

Medrxiv : the Preprint Server for Health Sciences
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing baseline models in outbreak forecasting significantly improves accuracy. A specific flatline model using recent transformed data outperformed standard benchmarks, highlighting the need for transparency in forecasting methods.

Related Experiment Videos

Last Updated: Mar 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Area of Science:

  • Epidemiology
  • Public Health
  • Computational Biology

Background:

  • Outbreak forecasting aids public health decision-making during epidemics.
  • Collaborative forecast hubs are the standard for real-time epidemic predictions.
  • Baseline models serve as crucial benchmarks within these hubs but their design is often subjective and understudied.

Purpose of the Study:

  • To evaluate the impact of subjective Baseline model design decisions on forecast performance.
  • To identify optimal Baseline model specifications for trend forecasting.
  • To compare trend baseline models with seasonal baseline models for specific surveillance targets.

Main Methods:

  • Retrospective forecasting was performed using multiple years of data for COVID-19, influenza, and RSV hospital admissions, and weighted influenza-like illness (wILI).
  • Three key Baseline specification decisions were evaluated: amount of historical data, data transformation, and forecast variant (flatline vs. drift).
  • Trend baseline models were compared to a seasonal baseline model for wILI data.

Main Results:

  • Model specification significantly altered forecast performance across all targets.
  • An optimal performing model was identified as a flatline model using 6-12 transformed recent observations.
  • This optimal model outperformed the current standard Baseline by an average of 9.6% and a seasonal baseline by 32.3%.

Conclusions:

  • Subjective Baseline design choices materially influence forecast accuracy and model rankings within collaborative hubs.
  • Increased transparency in Baseline model specifications is necessary.
  • Routine inclusion of multiple benchmark models in forecast hubs is recommended.