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...

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:

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Regression Analysis

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

You might also read

Related Articles

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

Sort by

Same author

Nonlinear dynamical analysis of chaos, fractal complexity, and entropy in EEG signals during music therapy in ICU burn patients.

Biomedical physics & engineering express·2026

Same author

EEGOpt: A performance efficient Bayesian optimization framework for automated EEG signal classification.

Computers in biology and medicine·2025

Same author

Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

Computers in biology and medicine·2025

Same author

Prospective treatment of Parkinson's disease by a siRNA-LDH nanoconjugate.

MedChemComm·2019

Same author

Glyphosate applied preharvest induces shikimic acid accumulation in hard red spring wheat (Triticum aestivum).

Journal of agricultural and food chemistry·2003

Search research articles

Related Experiment Video

Updated: Dec 11, 2025

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 python based support vector regression model for prediction of COVID19 cases in India.

Debanjan Parbat¹, Monisha Chakraborty²

¹Research Scholar, Biomedical Instrumentation Lab, School of Bioscience & Engineering, Jadavpur University, Kolkata, India.

Chaos, Solitons, and Fractals

|August 25, 2020

Summary

This study used support vector regression to predict COVID-19 cases, deaths, and recoveries. The model achieved high accuracy, suggesting a Gaussian decrease and a potential 3-4 month decline to minimal levels.

Keywords:

COVID19 Data analysis India Machine learining Python RBF Support vector regression

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Dec 11, 2025

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

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Epidemiology
Data Science
Machine Learning

Background:

Accurate prediction of infectious disease trends is crucial for public health response.
The early stages of the COVID-19 pandemic required robust modeling for forecasting.
Support Vector Regression (SVR) offers a powerful tool for time-series prediction.

Purpose of the Study:

To predict the number of total deaths, recovered cases, cumulative confirmed cases, and daily new cases of COVID-19.
To evaluate the performance of a Support Vector Regression model for epidemiological forecasting.
To forecast COVID-19 trends up to June 30, 2020.

Main Methods:

Utilized Support Vector Regression (SVR) with a Radial Basis Function kernel.
Trained the model on COVID-19 data from March 1 to April 30, 2020 (61 days).
Split data into 60% training and 40% testing sets, with a 10% confidence interval for curve fitting.

Main Results:

The SVR model demonstrated high accuracy: >97% for deaths, recoveries, and cumulative cases; 87% for daily new cases.
Predicted values were generated for the period up to June 30, 2020.
The model indicated a Gaussian decrease in cases, suggesting a potential 3-4 month period for cases to reach minimal levels.

Conclusions:

Support Vector Regression is an efficient and accurate method for predicting COVID-19 epidemiological trends.
The model's performance surpasses that of linear or polynomial regression.
The findings provide valuable insights for public health planning and resource allocation during the pandemic.