Abstract
Corona virus disease 2019 (COVID-19) is an infectious disease and has spread over more than 200 countries since its outbreak in December 2019. This pandemic has posed the greatest threat to global public health and seems to have changing characteristics with altering variants, hence various epidemiological and statistical models are getting developed to predict the infection spread, mortality rate and calibrating various impacting factors. But the aysmptomatic patient counts and demographical factors needs to be considered in model evaluation. Here we have proposed a new seven compartmental model, Susceptible- Exposed- Infected–Asymptomatic–Quarantined–Fatal–Recovered (SEIAQFR) which is based on classical Susceptible-Infected-Recovered (SIR) model dynamic of infectious disease, and considered factors like asymptomatic transmission and quarantine of patients. We have taken UK, US and India as a case study for model evaluation purpose. In our analysis, it is found that the Reproductive Rate (R0) of the disease is dynamic over a long period and provides better results in model performance (>0.98 R-square score) when model is fitted across smaller time period. On an average 40%−50% cases are asymptomatic and have contributed to model accuracy. The model is employed to show accuracy in correspondence with different geographic data in both wave of disease spread. Different disease spreading factors like infection rate, recovery rate and mortality rate are well analyzed with best fit of real world data. Performance evaluation of this model has achieved good R-Square score which is 0.95−0.99 for infection prediction and 0.90−0.99 for death prediction and an average 1%−5% MAPE in different wave of the disease in UK, US and India.
•New seven compartmental model- SEIAQFR.Model based on classical SIR model dynamic of infectious disease.•Asymptomatic patients play a vital role in covid epidemic spread.•UK, US and India are used as case study for model evaluation purpose.•Model performs better when fitted over smaller time period.