This paper shows that the generalized logistic distribution model is derived from the SIR model under certain conditions. In the SIR model, there are uncertainties in predicting the final values for the number of infected population and the infectious parameter. However, by utilizing the information obtained from the generalized logistic distribution model, we can perform the SIR numerical computation more stably and more accurately. Applications to SARS and COVID-19  using this combined method are also introduced.
This paper shows that the generalized logistic distribution model is derived from the well-known compartment model, consisting of susceptible, infected and recovered compartments, abbreviated as the SIR model, under certain conditions. In the SIR model, there are uncertainties in predicting the final values for the number of infected population and the infectious parameter. However, by utilizing the information obtained from the generalized logistic distribution model, we can perform the SIR numerical computation more stably and more accurately. Applications to severe acute respiratory syndrome (SARS) and Coronavirus disease 2019 ( COVID-19 ) using this combined method are also introduced.
R
SIR model
well-known compartment model, consisting of susceptible, infected and recovered compartments, abbreviated as the SIR model,
R
SARS and
severe acute respiratory syndrome (SARS) and Coronavirus disease 2019 (
A
)