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Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

296
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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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.3K
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...
8.3K
Prediction Intervals01:03

Prediction Intervals

2.6K
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. 
2.6K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

824
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...
824
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

461
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
461
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

680
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:
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Related Experiment Videos

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.

Suyel Namasudra1, S Dhamodharavadhani2, R Rathipriya2

  • 1Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India.

Neural Processing Letters
|April 6, 2021
PubMed
Summary
This summary is machine-generated.

A new Nonlinear Autoregressive Neural Network Time Series (NAR-NNTS) model accurately forecasts COVID-19 cases. Trained with the Levenberg Marquardt algorithm, it offers superior prediction for public health decision-making.

Keywords:
Bayesian regularizationForecastingLevenberg MarquardtRegressionScaled conjugate gradientTraining algorithm

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • The COVID-19 pandemic necessitates effective tools for tracking and predicting infection spread.
  • Accurate forecasting of confirmed, recovered, and death cases is crucial for public health interventions.
  • Existing models may lack the precision required for real-time epidemiological decision-making.

Purpose of the Study:

  • To develop and evaluate a novel Nonlinear Autoregressive Neural Network Time Series (NAR-NNTS) model for COVID-19 case prediction.
  • To compare the efficacy of different training algorithms (SCG, LM, BR) for the NAR-NNTS model.
  • To provide a reliable tool for health consultants to manage the COVID-19 outbreak.

Main Methods:

  • Implementation of a Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model.
  • Training the NAR-NNTS model using Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM), and Bayesian Regularization (BR) algorithms.
  • Performance evaluation using Root Mean Square Error (RMSE), Mean Square Error (MSE), and correlation coefficient (R-value).

Main Results:

  • The NAR-NNTS model demonstrated capability in forecasting COVID-19 epidemiological data.
  • The Levenberg Marquardt (LM) training algorithm yielded superior performance compared to SCG and BR.
  • Quantitative metrics (RMSE, MSE, R-value) confirmed the effectiveness of the LM-trained NAR-NNTS model.

Conclusions:

  • The proposed NAR-NNTS model, particularly when trained with the LM algorithm, is a promising tool for predicting COVID-19 cases.
  • This model can enhance awareness and support health authorities in making informed decisions to control the pandemic.
  • Further research can explore advanced neural network architectures for improved epidemiological forecasting.