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

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

Artificial neural network modeling to predict corrective stress of a two-layer composite plate under fully reversed cyclic loading using the finite element method & morrow method.

Scientific reports·2026

Same author

Free transverse vibration analysis of Non-uniform Nano-Timoshenko beams rotating around two perpendicular axes using differential quadrature method.

Scientific reports·2026

Same author

A predictive framework for evaluating the thermophysical properties of multi-walled carbon nanotube nanofluids dispersed in a water-ethylene glycol 50:50 base fluid.

Scientific reports·2026

Same author

Organogelation to Hydrogelation: Effect of C-Terminal Functionalization of Peptides.

Journal of peptide science : an official publication of the European Peptide Society·2026

Same author

Neural responses to proxemic distance regulation in urban public spaces in the post-COVID context using a portable electroencephalogram.

Frontiers in psychology·2026

Same author

Enhanced tourist flow forecasting in Aosta Valley: A novel ensemble AI framework with adaptive temporal dynamics.

PloS one·2026

Search research articles

Related Experiment Video

Updated: Nov 20, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Prediction modelling of COVID using machine learning methods from B-cell dataset.

Nikita Jain¹, Srishti Jhunthra¹, Harshit Garg¹

¹Department of Computer Science & Engineering, Bharati Vidyapeeth's College of Engineering, 110063 New Delhi, India.

Results in Physics

|January 26, 2021

Summary

This study predicts SARS-CoV and SARS-CoV-2 using B-cells data and machine learning. Ensemble strategies achieved high accuracy, aiding in early detection of these coronaviruses.

Keywords:

AdaBoost B-cells COVD-19 Coronavirus Ensembles Gradient boosting K – nearest neighbors (KNN)Logistic regression Multilayer perceptron (MLP)Naïve Bayes Random forest SARS-CoV SARS-CoV-2 Support vector machine (SVM)XGBoost

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Cell-Free Dot Blot as a Practical and Adaptable Immunoassay Platform for the Detection of Antibody Response in Human and Animal Sera

Cell-Free Dot Blot as a Practical and Adaptable Immunoassay Platform for the Detection of Antibody Response in Human and Animal Sera

Published on: May 23, 2025

Related Experiment Videos

Last Updated: Nov 20, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Cell-Free Dot Blot as a Practical and Adaptable Immunoassay Platform for the Detection of Antibody Response in Human and Animal Sera

Cell-Free Dot Blot as a Practical and Adaptable Immunoassay Platform for the Detection of Antibody Response in Human and Animal Sera

Published on: May 23, 2025

Area of Science:

Immunology
Infectious Diseases
Computational Biology

Background:

Coronaviruses, including SARS-CoV and SARS-CoV-2, have caused a global pandemic with significant mortality.
Accurate and timely prediction of coronavirus infections is crucial for disease management.

Purpose of the Study:

To predict the presence of SARS-CoV and SARS-CoV-2 using a B-cells dataset.
To evaluate the effectiveness of various machine learning and ensemble learning strategies for coronavirus prediction.

Main Methods:

Utilized a B-cells dataset for predicting SARS-CoV and SARS-CoV-2.
Applied diverse machine learning models: SVM, Naïve Bayes, K-nearest neighbors, AdaBoost, Gradient Boosting, XGBoost, Random Forest, and Neural Networks.
Developed and tested ensemble learning strategies to enhance prediction accuracy.

Main Results:

The proposed algorithm achieved an AUC score of 0.919 and 87.248% validation accuracy for SARS-CoV prediction.
For SARS-CoV-2 prediction, the algorithm yielded an AUC score of 0.923 and 87.7934% validation accuracy.
Ensemble learning strategies demonstrated significant benefits in predictive performance.

Conclusions:

Machine learning, particularly ensemble methods, can effectively predict SARS-CoV and SARS-CoV-2 using B-cells data.
The developed prediction models show high accuracy, supporting potential applications in early disease detection.
This approach offers a valuable tool for identifying individuals infected with these coronaviruses.