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Steps in Outbreak Investigation01:18

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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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Statistical Methods for Analyzing Epidemiological Data01:25

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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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Forecasting on Covid-19 infection waves using a rough set filter driven moving average models.

Saurabh Ranjan Srivastava1, Yogesh Kumar Meena1, Girdhari Singh1

  • 1Malviya National Institute of Technology, Jaipur, India.

Applied Soft Computing
|November 8, 2022
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Summary
This summary is machine-generated.

This study introduces a new algorithmic framework to predict COVID-19 infection waves and case counts. The displaced double moving average (DMA) and corrected moving average (SMA) algorithms offer high accuracy for public health planning.

Keywords:
Covid-19ForecastMoving averagePandemicRough set

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • The COVID-19 pandemic caused by SARS-COV-2 has led to millions of deaths globally.
  • Identifying and predicting COVID-19 infection waves is crucial for effective public health response.
  • Existing methods for wave detection and forecasting face challenges due to complex infection patterns.

Purpose of the Study:

  • To propose an algorithmic framework for forecasting the onset, progression, and end of COVID-19 infection waves.
  • To develop a novel technique for predicting daily new COVID-19 infection counts.
  • To provide accurate forecasts to aid in the allocation of healthcare resources.

Main Methods:

  • Utilized a displaced double moving average (DMA) algorithm to forecast COVID-19 wave dynamics.
  • Employed rough set theory to generate decision rules for marker detection in wave forecasting.
  • Introduced a corrected moving average (SMA) technique for predicting new infection counts.
  • Implemented and validated the framework on COVID-19 data from 12 countries up to January 31, 2022.

Main Results:

  • The DMA algorithm achieved a 94.08% precision in forecasting the rise and fall of COVID-19 waves.
  • The SMA algorithm demonstrated a mean absolute percentage error (MAPE) of 36.65% for next-day infection count prediction.
  • The framework requires a minimum observation window of 7 days for accurate forecasting.

Conclusions:

  • The proposed algorithmic framework accurately forecasts COVID-19 infection waves and case numbers.
  • These forecasting capabilities can significantly support healthcare administration in resource management.
  • The methods offer a valuable tool for navigating future pandemic waves and public health challenges.