Possibilities of exponential or Sigmoid growth of Covid19 data in different states of India

SUPRIYA MONDAL; Sabyasachi Ghosh

The copyright holder has placed this preprint (which was not . https://doi.org/10.1101/2020.04.10.20060442 doi: medRxiv preprint have considered only 15, whose total number of covid19 +ve cases are quite larger. Except KL remaining of them have been detected first covid19 positive cases from the beginning of March. Using this one month existing data of total and new cases for different states, we have attempted to visualize their future trends. Identifying the last trend of exponential growth of different states, we have listed their slope parameters (λ) from their respective time dependent total case plot. Though we have arranged our descriptions from MH to WB, based on total no of cases in an ascending order but they don't have same ascending order in their slope parameters, which is very important quantity for future spreading. In that context, TN, AP, MP, WB, having higher slope parameters, might face rapid growth of positive cases, while states having low slope parameter, like GJ, HR, PB might face slow growth. To zoom in the disaster side of this exponential growth we have extrapolated the total cases based on the last slope parameters and we estimated approximate months when entire population of the states might be infected. Due to the high slope parameter, TN and AP might reach this picture within May while WB, DL, TG, KL, MH, RJ, MP might reach on June, then UP, GJ on August; HR on September; PB on November. This order probably reflect the ranking of states, based on interventions, maintained by state residents.