Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
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
Ver todos los artículos relacionados

Video Experimental Relacionado

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

Evaluación de las previsiones de enfermedades infecciosas con normas de puntuación de asignación

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
Resumen
Este resumen es generado por máquina.

El desarrollo de nuevas métricas de evaluación de pronósticos es crucial para optimizar la política de enfermedades infecciosas. Este estudio introduce una regla de puntuación de asignación que refleja mejor el éxito de las políticas para minimizar las necesidades médicas no cubiertas, superando las medidas de precisión tradicionales.

Palabras clave:
Ciencias de la saludEvaluación de las previsionesReglas de puntuación adecuadassalud públicaasignación de recursos

Más Videos Relacionados

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

Videos de Experimentos Relacionados

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

Área de la Ciencia:

  • Epidemiología
  • Salud pública
  • Economía de la salud

Sus antecedentes:

  • La predicción de enfermedades infecciosas es vital para la política de salud pública.
  • Las métricas de evaluación de previsiones existentes pueden no alinearse con los objetivos de la política, como la asignación de recursos.
  • Existe una investigación limitada sobre el vínculo entre la precisión de los pronósticos y los resultados de las políticas en el mundo real.

Objetivo del estudio:

  • Explorar el vínculo entre las previsiones de enfermedades infecciosas y las decisiones políticas.
  • Desarrollar y evaluar una nueva regla de puntuación de pronósticos basada en la asignación de recursos.
  • Evaluar si esta nueva métrica capta mejor la utilidad prevista para la política que las medidas tradicionales.

Principales métodos:

  • Se utilizaron pronósticos probabilísticos de la carga regional de enfermedades (por ejemplo, hospitalizaciones por COVID-19).
  • Desarrolló una regla de puntuación de asignación para optimizar la distribución de recursos médicos limitados, minimizando las necesidades no satisfechas.
  • Comparación de las clasificaciones de pronóstico de la regla de puntuación de asignación con la puntuación del intervalo ponderado.

Principales resultados:

  • La regla de puntuación de asignación produjo diferentes clasificaciones de habilidades de pronóstico en comparación con la puntuación del intervalo ponderado.
  • Esto sugiere que la regla de asignación captura el valor de pronóstico perdido por las métricas de precisión tradicionales.
  • Las previsiones optimizadas para la asignación de recursos demostraron un mejor rendimiento relevante para las políticas.

Conclusiones:

  • Las métricas tradicionales de precisión de los pronósticos pueden no representar plenamente el valor de un pronóstico para la política.
  • Una regla de puntuación de asignación, directamente vinculada al desempeño de las políticas, es un enfoque prometedor para la evaluación de las previsiones de epidemias.
  • El diseño de reglas de puntuación vinculadas a objetivos políticos puede mejorar la utilidad de los pronósticos de enfermedades infecciosas.