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Bounding pandemic spread by heat spread.

Teddy LazebnikUri Itai
Published in: Journal of engineering mathematics (2023)
The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread as indicated by the Susceptible-Infected-Recovered (SIR) model. We study the spread of a pandemic on an interaction (social) graph as a diffusion and compared it with the stochastic SIR model. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.
Keyphrases
  • sars cov
  • coronavirus disease
  • healthcare
  • mental health
  • public health
  • oxidative stress
  • heat stress
  • machine learning
  • health information
  • climate change
  • convolutional neural network
  • human health