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.

Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

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...

You might also read

Related Articles

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

Sort by

Same author

COVID-19 Epidemic in Sri Lanka: A Mathematical and Computational Modelling Approach to Control.

Computational and mathematical methods in medicine·2020

Same author

Phenomenological Modelling of COVID-19 Epidemics in Sri Lanka, Italy, the United States, and Hebei Province of China.

Computational and mathematical methods in medicine·2020

Same author

Simulation Model for Dynamics of Dengue with Innate and Humoral Immune Responses.

Computational and mathematical methods in medicine·2018

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 25, 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

Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average

A M C H Attanayake¹, S S N Perera²

¹Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.

Biomed Research International

|December 21, 2020

Summary

The Alpha-Sutte Indicator approach accurately models and predicts cumulative COVID-19 cases, outperforming ARIMA. This method aids health authorities in managing the pandemic effectively.

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Nov 25, 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

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Epidemiology
Mathematical modeling
Public health

Background:

The COVID-19 pandemic's rapid spread necessitates effective control strategies.
No definitive cure exists, making accurate case prediction crucial for mitigation efforts.
Diverse global socioeconomic and geographical factors influence disease transmission.

Purpose of the Study:

To model and predict cumulative COVID-19 cases using the Alpha-Sutte Indicator approach.
To compare the efficacy of the Alpha-Sutte Indicator against the ARIMA method.
To provide reliable short-term forecasts for public health management.

Main Methods:

Utilized the Alpha-Sutte Indicator approach for COVID-19 case modeling.
Selected eight diverse countries (USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, South Africa).
Validated the model using 10% of cumulative case data up to September 26, 2020.

Main Results:

The Alpha-Sutte Indicator demonstrated superior performance with low Mean Absolute Percentage Errors (MAPE) across all selected countries.
MAPE values ranged from 0.03% (Pakistan) to 1.28% (Spain).
Paired t-tests confirmed no statistically significant differences between forecasted and real cases, validating the model's effectiveness.

Conclusions:

The Alpha-Sutte Indicator approach is highly suitable for short-term forecasting of cumulative COVID-19 incidences.
The model's accuracy and reliability support its recommendation for public health decision-making.
Predictions generated can assist healthcare authorities in pandemic control and management strategies.