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

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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:

Infection

Infection

When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...

You might also read

Related Articles

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

Sort by

Same author

An eyestalk-enriched GATA factor (NdGATA123c) modulates photosensitivity and molting in Neocaridina denticulata sinensis.

The Journal of experimental biology·2026

Same author

A water and land resources multi-objective optimal allocation model based on ecological, economic and social sustainability using NSGA-III.

Scientific reports·2026

Same author

ROS-Mediated Controllable Targeted Protein Degradation: Progress and Reflections.

Journal of medicinal chemistry·2026

Same author

Chinese herbal and natural compounds for Parkinson's disease: Myth or truth.

Journal of ethnopharmacology·2026

Same author

Formation of polyhedral photonic clusters driven by interfacial energy control in confined assembly.

Journal of colloid and interface science·2026

Same author

Maintenance hemodialysis patients' nutritional literacy and its relationship with frailty: a cross-sectional study based on latent profile analysis.

Frontiers in nutrition·2026

Search research articles

Related Experiment Video

Updated: Jul 20, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

An innovative ensemble model based on deep learning for predicting COVID-19 infection.

Xiaoying Su¹, Yanfeng Sun², Hongxi Liu¹

¹School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.

Scientific Reports

|July 29, 2023

Summary

This study introduces WOCLSA, a novel deep learning model for predicting COVID-19 infection using patient data. WOCLSA demonstrates superior accuracy and efficiency compared to other ensemble models, aiding public health crisis management.

More Related Videos

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

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Jul 20, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Epidemiology

Background:

Global public health crises necessitate accurate disease prediction for effective resource allocation and diagnosis.
Current COVID-19 prediction models, often single or epidemiological, show limitations in accuracy.
Existing ensemble models have room for performance improvement, and few utilize laboratory results for prediction.

Purpose of the Study:

To develop an innovative deep learning model for enhanced disease prediction accuracy.
To address the limitations of existing models in predicting infectious diseases like COVID-19.
To explore the use of patient laboratory indicators in disease prediction models.

Main Methods:

Proposed the Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) (WOCLSA) model.
Utilized the Whale Optimization Algorithm to optimize parameters (neuron number, dropout, batch size) for the integrated ANN, CNN, and LSTM model.
Employed 18 patient indicators as predictors and validated models using train-test split, comparing WOCLSA against three other ensemble deep learning models.

Main Results:

WOCLSA achieved high prediction performance, with Area Under the Curve (AUC) reaching 91%, 91%, and 93%.
Other performance metrics including accuracy, F1 score, precision, and recall consistently exceeded 91%, outperforming comparable models.
The WOCLSA model demonstrated an advantage in execution time, indicating computational efficiency.

Conclusions:

The WOCLSA ensemble model shows significant potential for assisting in the verification of laboratory results and predicting various diseases during public health events.
The model's high accuracy and efficiency offer a valuable tool for enhancing disease surveillance and management.
Further research should focus on expanding the application of such advanced medical disease prediction models.