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:

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

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:

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.

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Causality in Epidemiology

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

You might also read

Related Articles

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

Sort by

Same author

Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms.

Multimedia tools and applications·2023

Same author

Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method.

Applied biochemistry and biotechnology·2019

Same author

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study.

Applied biochemistry and biotechnology·2019

Same author

Classification of Skin Disease using Ensemble Data Mining Techniques.

Asian Pacific journal of cancer prevention : APJCP·2019

Search research articles

Related Experiment Video

Updated: Dec 5, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions.

Vikas Chaurasia¹, Saurabh Pal¹

¹Department of Computer Applications, VBS Purvanchal University, Jaunpur, India.

SN Computer Science

|October 16, 2020

Summary

This study estimates COVID-19 mortality rates using ARIMA and regression models. Findings indicate a declining trend in death cases after May 2020, aiding future pandemic preparedness.

Keywords:

ARIMA model Breath COVID-19 Epidemic Humanity RMSE Regression model

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

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Dec 5, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

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

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

The COVID-19 pandemic has escalated globally, leading to millions of deaths and posing a significant threat to humanity.
Urgent need for accurate mortality rate estimation to inform government and hospital preparedness strategies.

Purpose of the Study:

To estimate the mortality rate of COVID-19 using statistical models.
To analyze trends in death cases and provide forecasts for future planning.

Main Methods:

Utilized the ARIMA (AutoRegressive Integrated Moving Average) model for time-series forecasting.
Employed a regression model to estimate and compare predicted versus actual COVID-19 death cases.
Dataset collected from DataHub-Novel Coronavirus 2019-Dataset (January 22 - June 29, 2020).
Model validation using correlation coefficients including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).

Main Results:

The ARIMA model demonstrated strong predictive accuracy, with the average absolute percentage error validating the model at 99.09%.
Analysis revealed a continuous decline in COVID-19 death cases.
The regression model indicated a decrease in the predicted mortality rate after May 2, 2020.

Conclusions:

Statistical modeling, specifically ARIMA and regression, can effectively estimate and forecast COVID-19 mortality rates.
The predicted decline in mortality post-May 2020 provides valuable insights for public health planning.
These models offer a basis for short-term and long-term mortality forecasting to support governmental and medical responses.