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Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

Ashokkumar Palanivinayagam1, Ramesh Kumar Panneerselvam2, P J Kumar3

  • 1Sri Ramachandra Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India.

Computational and Mathematical Methods in Medicine
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Summary
This summary is machine-generated.

This study proposes a graph theory algorithm to optimize COVID-19 relief fund distribution, ensuring social distancing. An LSTM model predicts disease spread, aiding public health strategies.

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Area of Science:

  • Public Health
  • Epidemiology
  • Computer Science

Background:

  • The COVID-19 pandemic significantly impacted India, necessitating public health interventions.
  • Vaccine hesitancy and socio-economic challenges complicated disease control efforts.
  • Government relief schemes faced difficulties in maintaining social distancing due to large populations.

Purpose of the Study:

  • To develop an algorithm for scheduling COVID-19 relief funds to ensure social distancing.
  • To utilize deep learning for predicting COVID-19 spread rates and analyzing daily cases.

Main Methods:

  • Graph theory was applied to schedule relief fund distribution efficiently.
  • Long Short-Term Memory (LSTM) deep learning models were employed for epidemiological forecasting.

Main Results:

  • The proposed graph theory algorithm facilitates social distancing during relief distribution with available staff.
  • LSTM models provide insights into COVID-19 spread dynamics and daily case analysis.

Conclusions:

  • Optimized scheduling of relief funds can mitigate transmission risks during aid distribution.
  • Predictive modeling aids in understanding and managing the COVID-19 pandemic's trajectory.