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:

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.

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by

Same author

Automated low-cost framework for crack measurements in RC structures using deep learning approach.

Scientific reports·2026

Same author

Broad-spectrum antiviral potential of vitexin and isovitexin from Jatropha integerrima: in vitro cytoprotective effects and in silico insights.

Naunyn-Schmiedeberg's archives of pharmacology·2026

Same author

Concomitant Cardiac Transthyretin Amyloidosis and Coronary Artery Disease---Imaging Features and Pitfalls.

CJC open·2026

Same author

Dichlorophthalic Anhydride Derivatives as Anti-Apoptotic and Antiproliferative Agents by Multi-Targeted Mechanism: Biological Evaluation and Molecular Docking Studies.

Journal of biochemical and molecular toxicology·2026

Same author

The gene-modulating power of Tannins isolated from Jatropha integerrima flowers on the transcriptomic profile of multidrug-resistant Klebsiella pneumoniae.

Scientific reports·2026

Same author

LC/ESI-MS/MS phytochemical profiling and apoptotic effect of Haloxylon scoparium leaf extract on hepatocellular carcinoma.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Oct 26, 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

Deep learning model for forecasting COVID-19 outbreak in Egypt.

Mohamed Marzouk¹, Nehal Elshaboury², Amr Abdel-Latif³

¹Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt.

Process Safety and Environmental Protection : Transactions of the Institution of Chemical Engineers, Part B

|August 2, 2021

Summary

Artificial intelligence models, including LSTM, accurately predicted COVID-19 spread in Egypt. The LSTM model showed the best performance in forecasting infections, aiding public health policy development.

Keywords:

COVID-19 Deep learning Egypt Epidemic model Long short-term memory network

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

Related Experiment Videos

Last Updated: Oct 26, 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

Area of Science:

Epidemiology
Artificial Intelligence
Public Health

Background:

COVID-19 declared a global pandemic in early 2020.
Understanding epidemiological characteristics is vital for containment.
Egypt faced significant COVID-19 outbreaks.

Purpose of the Study:

To predict COVID-19 prevalence in Egypt using AI models.
To evaluate the performance of LSTM, CNN, and MLP neural networks.
To forecast future infection trends for policy-making.

Main Methods:

Application of Long Short-Term Memory (LSTM) network.
Utilized Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP).
Models trained and validated on data from February 2020 to August 2020.

Main Results:

LSTM demonstrated superior performance in forecasting cumulative infections.
The model accurately predicted infections one week and one month ahead.
Forecasts for July 2021 estimated 285,939 infections, 234,747 recoveries, and 17,251 deaths.

Conclusions:

AI, particularly LSTM, is effective for predicting COVID-19 outbreaks.
The study provides valuable data for Egyptian public health decision-makers.
Forecasting capabilities can inform and monitor disease control strategies.