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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

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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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
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Time-Series Graph

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Related Experiment Video

Updated: Nov 7, 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

A COVID-19 time series forecasting model based on MLP ANN.

Pedro Henrique Borghi^1,2, Oleksandr Zakordonets¹, João Paulo Teixeira¹

¹Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politecnico de Bragança, Braganca, Portugal.

Procedia Computer Science

|May 3, 2021

Summary

This study introduces a machine learning model using a multilayer Perceptron artificial neural network to predict COVID-19 cases and deaths up to six days in advance. The model, trained on data from 30 countries, aids in pandemic response and public awareness.

Keywords:

COVID-19 Brazil forecast COVID-19 Italy forecast COVID-19 worldwide forecast

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

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

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Published on: December 9, 2015

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

Area of Science:

Epidemiology
Artificial Intelligence
Computational Biology

Background:

The rapid global spread of COVID-19 necessitates accurate predictive tools for informed public health and administrative action.
Effective forecasting of infected cases and deaths is crucial for timely interventions and resource allocation.

Purpose of the Study:

To develop and evaluate a machine learning model for predicting COVID-19 trends.
To provide a tool for administrative sectors and the public to better understand and respond to the pandemic.

Main Methods:

Utilized a multilayer Perceptron artificial neural network for time series prediction.
Trained the model on accumulated and new COVID-19 cases and deaths from 30 countries over a 20-day context.
Applied a moving average filter and maximum value normalization to preprocess the data.

Main Results:

The artificial neural network model demonstrated effectiveness in predicting COVID-19 case and death trajectories.
Predictions were made for time series up to six days into the future.
Model performance was assessed using Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics.

Conclusions:

The developed machine learning model shows promise for short-term COVID-19 forecasting.
The study aims to make these predictions accessible online to support the ongoing fight against the pandemic.