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

Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

You might also read

Related Articles

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

Sort by

Same author

Comment on Yu et al. Reproductive Lifespan and Reproductive Factors in Relation to Dementia Risk in Postmenopausal Women With Type 2 Diabetes.

Diabetes care·2026

Same author

Dried blood spot quality in newborn inherited metabolic disease screening in Beijing, 2020-2024: a retrospective analysis of incidence, causes, and improvement strategies.

BMC pediatrics·2026

Same author

Beyond abscess and inflammatory nodule counts: Reflecting on placebo and pain in the ruxolitinib trial.

Journal of the American Academy of Dermatology·2026

Same author

Sex Differences in the Prevalence and Associated Factors of Metabolic Syndrome: A Cross-Sectional Study of University Employees in China.

Diabetes, metabolic syndrome and obesity : targets and therapy·2026

Same author

Platelet-related hematologic markers and genetic associations of aspirin resistance in kawasaki disease.

Frontiers in molecular biosciences·2026

Same author

Rural-to-urban migrant worker mobility shaped measles epidemics in China.

PLoS computational biology·2026

Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026

Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026

Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026

Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026

Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026

Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

High-throughput Detection Method for Influenza Virus

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

Type- and Subtype-Specific Influenza Forecast.

Sasikiran Kandula, Wan Yang, Jeffrey Shaman

American Journal of Epidemiology

|February 9, 2017

Summary

This summary is machine-generated.

Forecasting influenza by type and subtype improves prediction accuracy compared to total incidence. This detailed monitoring aids public health officials in better planning and responding to influenza activity.

Keywords:

forecast influenza influenza subtype influenza type peak intensity peak week

More Related Videos

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Published on: January 30, 2017

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Published on: July 26, 2019

Related Experiment Videos

Last Updated: Mar 7, 2026

High-throughput Detection Method for Influenza Virus

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Published on: January 30, 2017

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Published on: July 26, 2019

Area of Science:

Epidemiology
Public Health
Infectious Disease Modeling

Background:

Infectious disease incidence prediction has advanced, increasing public health utility.
Operational real-time influenza forecasts are available at municipal and state levels in the US.
Current forecasts use ensemble simulations optimized with observational data and data assimilation.

Purpose of the Study:

To compare the accuracy of influenza forecasts by type/subtype versus total incidence.
To evaluate forecast performance for influenza during the 2003-2015 period in the United States.

Main Methods:

Utilized ensemble simulations for influenza transmission dynamics.
Optimized forecasts using historical influenza incidence data and data assimilation.
Analyzed forecast accuracy for specific influenza types/subtypes and total incidence.

Main Results:

Forecasts separated by influenza type/subtype were generally more accurate.
Summed type/subtype forecasts also provided more accurate predictions of total influenza incidence.
Monitoring influenza by type/subtype enhances observational content and forecasting accuracy.

Conclusions:

Forecasting influenza by type and subtype leads to more accurate predictions.
Detailed monitoring supports improved public health responses and planning for influenza.
Enhanced forecasting accuracy is crucial for effective management of current and future influenza seasons.