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

168
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:
168
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

475
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:
475
Causality in Epidemiology01:21

Causality in Epidemiology

626
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
626
Random Error01:04

Random Error

1.3K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
1.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
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...
8.2K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Real-time forecasting of data revisions in epidemic surveillance streams.

PLoS computational biology·2025
Same author

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Nature communications·2024
Same author

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

medRxiv : the preprint server for health sciences·2024
Same author

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

An open repository of real-time COVID-19 indicators.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Proceedings of the National Academy of Sciences of the United States of America·2021
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Aug 17, 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

10.7K

Recalibrating probabilistic forecasts of epidemics.

Aaron Rumack1, Ryan J Tibshirani1,2, Roni Rosenfeld1

  • 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology
|December 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a recalibration method to improve the reliability of distributional forecasts, especially for epidemic prediction. The technique enhances forecast accuracy and calibration, offering a robust post-processing tool.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

298

Related Experiment Videos

Last Updated: Aug 17, 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

10.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

298

Area of Science:

  • Epidemiology
  • Computational Statistics
  • Data Science

Background:

  • Distributional forecasts are crucial for applications like epidemic prediction.
  • Forecasts often suffer from miscalibration, leading to unreliable uncertainty estimates.
  • Improving forecast reliability is essential for public health decision-making.

Purpose of the Study:

  • To present a novel recalibration method for black-box distributional forecasters.
  • To extend the method for enhanced epidemic forecast recalibration.
  • To demonstrate improvements in forecast calibration and accuracy.

Main Methods:

  • Developed a recalibration technique applicable to existing forecasters using retrospective data.
  • Incorporated an extension specifically for improving epidemic forecast reliability.
  • Validated the method using in-sample training and measurement.

Main Results:

  • The recalibration method is guaranteed to improve calibration and log score performance in-sample.
  • Proven that the increase in expected log score equals the entropy of the Probability Integral Transform (PIT) distribution.
  • Demonstrated reliable improvements in forecast accuracy and calibration across 27 influenza forecasters in the FluSight Network.

Conclusions:

  • The presented recalibration method effectively enhances the reliability of distributional forecasts.
  • The method serves as a robust and user-friendly post-processing tool for epidemic forecasting.
  • This approach offers a significant advancement in making epidemic predictions more accurate and trustworthy.