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Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

405
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:
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Prediction Intervals01:03

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.
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. 
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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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...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Related Experiment Videos

Neural network powered COVID-19 spread forecasting model.

Michał Wieczorek1, Jakub Siłka1, Marcin Woźniak1

  • 1Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland.

Chaos, Solitons, and Fractals
|August 25, 2020
PubMed
Summary

This study introduces a Neural Network model for predicting COVID-19 spread, achieving over 99% accuracy. The model aids in planning public health actions by forecasting virus transmission dynamics.

Keywords:
60G2568T0568T37COVID-19Neural networkPrediction

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Predicting virus spread is crucial for effective public health interventions.
  • COVID-19 transmission is influenced by complex environmental and social factors.
  • Accurate forecasting models are needed to manage pandemic responses.

Purpose of the Study:

  • To develop and evaluate a Neural Network model for COVID-19 spread prediction.
  • To provide accurate predictions at both national and regional levels.
  • To enhance epidemiological forecasting capabilities.

Main Methods:

  • Utilized a deep architecture Neural Network model.
  • Employed the NAdam training algorithm for model optimization.
  • Trained the model using official governmental and open-source COVID-19 data.

Main Results:

  • The developed model demonstrates high accuracy in COVID-19 spread prediction.
  • Prediction accuracy reached over 99% in certain cases.
  • The model provides forecasts for both countries and regions.

Conclusions:

  • The proposed Neural Network model is effective for COVID-19 spread prediction.
  • Accurate predictions can support proactive public health planning.
  • The model offers a valuable tool for epidemiological surveillance.