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

188
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:
188
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

180
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...
180
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

530
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:
530
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
174
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

331
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
331
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

154
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
154

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

Neuronal lipid droplets play a conserved and sex-biased role in maintaining whole-body energy homeostasis.

Nature metabolism·2026
Same author

Baseline nowcasting methods for handling delays in epidemiological data.

Wellcome open research·2026
Same author

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

Statistics in medicine·2026
Same journal

Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Professor Ian Hall's contribution to the Discussion of 'Some statistical aspects of the COVID-19 response' by Wood et al.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

Multivariate mixed models accounting for don't know options in ordinal data.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same journal

A Bayesian zero-inflated spatially varying coefficients model for overdispersed binomial data.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2025
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

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

Evaluating infectious disease forecasts with allocation scoring rules.

Aaron Gerding1, Nicholas G Reich1, Benjamin Rogers1

  • 1Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts at Amherst, Amherst, Massachusetts, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

Developing novel forecast evaluation metrics is crucial for optimizing infectious disease policy. This study introduces an allocation scoring rule that better reflects policy success in minimizing unmet medical needs, outperforming traditional accuracy measures.

Keywords:
epidemiologyforecast evaluationproper scoring rulespublic healthresource allocation

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Related Experiment Videos

Last Updated: Sep 9, 2025

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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Area of Science:

  • Epidemiology
  • Public Health
  • Health Economics

Background:

  • Infectious disease forecasting is vital for public health policy.
  • Existing forecast evaluation metrics may not align with policy goals like resource allocation.
  • Limited research exists on linking forecast accuracy to real-world policy outcomes.

Purpose of the Study:

  • To explore the link between infectious disease forecasts and policy decisions.
  • To develop and evaluate a novel forecast scoring rule based on resource allocation.
  • To assess if this new metric better captures forecast utility for policy than traditional measures.

Main Methods:

  • Utilized probabilistic forecasts of regional disease burden (e.g., COVID-19 hospitalizations).
  • Developed an allocation scoring rule to optimize limited medical resource distribution, minimizing unmet need.
  • Compared forecast rankings from the allocation scoring rule against the weighted interval score.

Main Results:

  • The allocation scoring rule produced different forecast skill rankings compared to the weighted interval score.
  • This suggests the allocation rule captures forecast value missed by traditional accuracy metrics.
  • Forecasts optimized for resource allocation demonstrated improved policy-relevant performance.

Conclusions:

  • Traditional forecast accuracy metrics may not fully represent a forecast's value for policy.
  • An allocation scoring rule, directly tied to policy performance, is a promising approach for epidemic forecast evaluation.
  • Designing scoring rules linked to policy objectives can enhance the utility of infectious disease forecasts.