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

Dialyzer-associated thrombocytopenia: Case report and brief review of the literature.

Journal of family medicine and primary care·2026

Same author

Diagnostic potential of salivary biomarkers for primary biliary cholangitis: a systematic review.

Frontiers in medicine·2026

Same author

Cross-sectional study for assessment of knowledge, attitudes and practices of chronic kidney disease patients toward potassium-rich diet intake in Jazan-Saudi Arabia.

Medicine·2025

Same author

Prevalence and assessment of risk factors of chronic kidney disease in the ASIR region of Saudi Arabia.

Annals of medicine and surgery (2012)·2024

Same author

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models.

Sensors (Basel, Switzerland)·2024

Same author

Role of Optimization in RNA-Protein-Binding Prediction.

Current issues in molecular biology·2024

Same journal

The association between antiretroviral therapy adherence following cancer diagnosis and survival outcomes in people living with HIV and cancer in Shanghai, China.

Journal of infection and public health·2026

Same journal

Comparative metabolic safety of efavirenz-, dolutegravir-, and bictegravir-based antiretroviral regimens in HIV-infected adults: Evidence for weight-dependent lipid deterioration.

Journal of infection and public health·2026

Same journal

Prioritizing tuberculosis preventive therapy for IGRA-negative people living with HIV initiating antiretroviral therapy.

Journal of infection and public health·2026

Same journal

Sepsis-induced neuromuscular dysfunction: A systematic review with meta-analysis.

Journal of infection and public health·2026

Same journal

Clinical phenotypes and mortality predictors in pediatric critical pertussis: A multinational study.

Journal of infection and public health·2026

Same journal

Submicroscopic malaria infections and inadequate IPTp-SP coverage among pregnant women in rural Burkina Faso: An observational study.

Journal of infection and public health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 14, 2026

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Building predictive models for MERS-CoV infections using data mining techniques.

Isra Al-Turaiki¹, Mona Alshahrani¹, Tahani Almutairi¹

¹Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia.

Journal of Infection and Public Health

|September 20, 2016

Summary

This summary is machine-generated.

Data mining models predict MERS-CoV recovery and stability. Healthcare workers show higher survival rates, while age and symptoms are key factors for infection stability in MERS-CoV patients.

Keywords:

Classification Data mining Decision tree J48 MERS-CoV Naive Bayes

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Related Experiment Videos

Last Updated: Mar 14, 2026

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

Epidemiology
Data Mining
Public Health

Background:

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreaks in Saudi Arabia have raised global health concerns.
MERS-CoV, a novel virus in the coronavirus family, can cause severe, fatal complications, yet remains poorly understood.
Existing data mining techniques are applied to analyze MERS-CoV infection patterns.

Purpose of the Study:

To understand the stability and recovery possibilities of MERS-CoV infections.
To build predictive models for MERS-CoV patient outcomes.
To identify key factors influencing MERS-CoV infection progression.

Main Methods:

Utilized Naive Bayes classifier and J48 decision tree algorithms for model development.
Analyzed a dataset of 1082 MERS-CoV cases from 2013-2015.
Split data to predict recovery (recovery/death) and stability (current status).

Main Results:

Recovery models suggest healthcare workers have a higher survival likelihood, possibly due to vaccinations.
J48 stability models identified symptomatic status and patient age as critical predictors.
Model accuracy ranged from 53.6% to 71.58%, evaluated using accuracy, precision, and recall.

Conclusions:

Enhanced prediction model performance is achievable with larger patient datasets.
Future research will involve direct data collection from hospitals in Riyadh for more comprehensive analysis.