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Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model.

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Summary
This summary is machine-generated.

This study introduces an algorithm to identify and adjust anomalous COVID-19 case data in India. This improves the accuracy of epidemiological models for better healthcare resource management.

Keywords:
AdjustmentCOVID-19Epidemiological modelsJumps and dropsModified SIS model

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • Accurate COVID-19 case prediction is crucial for India's healthcare resource management.
  • Epidemiological models are vital for epidemic control but rely on accurate historical data.
  • Erratic daily case counts can significantly reduce the predictive accuracy of these models.

Purpose of the Study:

  • To develop an automated algorithm for identifying anomalous daily COVID-19 case data (jumps and drops).
  • To adjust these anomalous data points to improve the accuracy of epidemiological models.
  • To enhance the prediction of cumulative COVID-19 infected cases in India.

Main Methods:

  • Proposed an algorithm to automatically detect anomalous 'jump' and 'drop' days in daily COVID-19 case data.
  • Adjusted the number of daily infected cases for anomalous days based on the overall trend.
  • Amended the training data with these adjusted observations.
  • Applied the algorithm with a modified Susceptible-Infected-Susceptible (SIS) model.

Main Results:

  • The algorithm successfully identified anomalous data points in the COVID-19 case reporting.
  • Adjusting the training data by correcting anomalous counts led to improved prediction accuracy.
  • The modified SIS model demonstrated enhanced predictive performance after data amendment.

Conclusions:

  • Automated identification and adjustment of anomalous COVID-19 case data significantly improve epidemiological model accuracy.
  • This approach offers a valuable tool for more reliable forecasting of infectious disease trends.
  • Enhanced prediction accuracy aids in better planning and management of healthcare resources during epidemics.