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

You might also read

Related Articles

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

Sort by

Same author

Sequential CLAGE-Ven (cladribine, cytarabine, etoposide, and venetoclax) with reduced-intensity conditioning improves outcomes in patients with refractory acute myeloid leukemia: a prospective phase II study.

Bone marrow transplantation·2026

Same author

Basilic vein transposition to enhance arteriovenous fistula venous outflow: a single-center technical note.

BMC surgery·2026

Same author

Metamorphosis of the purple sea urchin Heliocidaris crassispina: Single-cell transcriptomic datasets.

Scientific data·2026

Same author

An Interdisciplinary Course Development in Nursing Informatics: A Hybrid Teaching Model.

Computers, informatics, nursing : CIN·2026

Same author

Characterization and genomic analysis of a novel Vibrio harveyi Vibrio phage LRZ.

Archives of virology·2026

Same author

Environmental Responses and Interspecific Associations of Fish Communities in the Zhoushan Fishing Ground Revealed by HMSC.

Animals : an open access journal from MDPI·2026

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Mouse Footpad Inoculation Model to Study Viral-Induced Neuroinflammatory Responses

Mouse Footpad Inoculation Model to Study Viral-Induced Neuroinflammatory Responses

Published on: June 14, 2020

Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and

Xialv Lin¹, Xiaofeng Wang², Yuhan Wang¹

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

International Journal of Environmental Research and Public Health

|April 3, 2021

Summary

This study introduces a machine learning model for predicting hand-foot-and-mouth disease (HFMD) infections. The new approach improves prediction accuracy and regional early-warning capabilities for this common childhood illness.

Keywords:

early-warning model genetic algorithm hand-foot-and-mouth disease neural network

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Nov 10, 2025

Mouse Footpad Inoculation Model to Study Viral-Induced Neuroinflammatory Responses

Mouse Footpad Inoculation Model to Study Viral-Induced Neuroinflammatory Responses

Published on: June 14, 2020

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Epidemiology
Public Health
Infectious Disease Modeling

Background:

Rapid economic development correlates with increased infectious disease spread.
Hand-foot-and-mouth disease (HFMD) poses significant health risks to children, especially infants.
Accurate prediction and early warning systems for HFMD are crucial for public health interventions.

Observation:

Current HFMD prediction models primarily use historical case data, neglecting influential factors.
Existing HFMD early-warning systems often rely on direct case reports and separate spatio-temporal statistical methods.
These traditional methods result in high error rates and low confidence in early-warning outcomes.

Findings:

Machine learning methods were employed to develop an HFMD epidemic prediction model.
Multiple early-warning models were explored and compared.
The proposed HFMD prediction algorithm demonstrated superior accuracy compared to existing methods.
An early-warning algorithm based on threshold comparison yielded effective results.

Implications:

The developed machine learning model offers enhanced accuracy for HFMD infection prediction.
Improved regional early-warning systems can mitigate the impact of HFMD outbreaks.
This research provides a foundation for more robust infectious disease surveillance and control strategies.