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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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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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Data Mining Based Techniques for Covid-19 Predictions.

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  • 1Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India.

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Summary
This summary is machine-generated.

Accurate forecasting models for COVID-19 are crucial for resource management and pandemic control. This study reviews various forecasting techniques, analyzing their strengths and weaknesses to aid in predicting cases and deaths.

Keywords:
Deep Learning ModelsSoft Computing-based ModelsStochastic Forecasting ModelsSupervised ML Models

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • The COVID-19 pandemic has caused significant global mortality and morbidity.
  • Accurate forecasting of COVID-19 cases and recovery rates is vital for effective public health responses and resource allocation.
  • The need for reliable short-term predictions to manage the pandemic's trajectory is paramount.

Purpose of the Study:

  • To present and categorize diverse forecasting techniques for COVID-19 cases.
  • To critically evaluate the advantages and disadvantages of various forecasting methodologies.
  • To offer insights into challenges associated with forecasting COVID-19 positive, negative, and death cases.

Main Methods:

  • Systematic review and classification of existing COVID-19 forecasting models.
  • Analysis of the strengths and limitations of different forecasting approaches.
  • Examination of potential issues in predicting pandemic-related case numbers.

Main Results:

  • Identified and categorized multiple forecasting techniques applicable to COVID-19.
  • Detailed the merits and demerits of each forecasting category.
  • Highlighted potential challenges in accurately predicting COVID-19 trends.

Conclusions:

  • A comprehensive understanding of forecasting techniques is essential for combating the COVID-19 pandemic.
  • The aggregation of findings from various models can enhance prediction accuracy.
  • Informed decision-making relies on robust and well-analyzed forecasting data.