About: The SIR epidemiological equations model new affected and removed cases as roughly proportional to the current number of infected cases. The present report adopts an alternative that has been considered in the literature, in which the number of new affected cases is proportional to the [≤] 1 power of the number of infected cases. After arguing that = 1 models exponential growth while < 1 models polynomial growth, a simple method for parameter estimation in differential equations subject to noise, the random-time transformation RTT of Bassan, Meilijson, Marcus and Talpaz 1997, will be reviewed and applied in an attempt to settle the question as to the nature of Covid19.   Goto Sponge  NotDistinct  Permalink

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  • The SIR epidemiological equations model new affected and removed cases as roughly proportional to the current number of infected cases. The present report adopts an alternative that has been considered in the literature, in which the number of new affected cases is proportional to the [≤] 1 power of the number of infected cases. After arguing that = 1 models exponential growth while < 1 models polynomial growth, a simple method for parameter estimation in differential equations subject to noise, the random-time transformation RTT of Bassan, Meilijson, Marcus and Talpaz 1997, will be reviewed and applied in an attempt to settle the question as to the nature of Covid19.
Subject
  • Zoonoses
  • Epidemiology
  • Viral respiratory tract infections
  • Growth curves
  • Mathematical modeling
  • COVID-19
  • Differential equations
  • Environmental social science
  • Exponentials
  • Estimation theory
  • Mathematical and quantitative methods (economics)
  • Occupational safety and health
  • Ordinary differential equations
  • Metric geometry
  • Signal processing
  • Cayley graphs
  • Infinite group theory
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