The outbreak of novel coronavirus officially named as "COVID-19" emerged as a distressing Lag time (t l ): This is the time duration between the first observed case and the time when 118 spreadability become significant. The data during the lag phase can be omitted during predictive 119 modelling, and the term (t l ) must be incorporated by replacing time "t" with (t+t l ) in the model. 2. The cumulative population in the study zone follows an "S"-shaped curve that passes through 124 an inflection point of time where the spreadability rate will be maximum. 126 The phenomenon of virus spread in society differs from microbial growth due to various identified that change in β considerably influenced the shape of the curve for the cumulative 156 population as well as spreadability rate, therefore, defining β as "shape factor" for the model. Table 1 . The suggested nested exponential mathematical model proved to be efficient by explaining the 199 COVID-19 outbreak with a high goodness of fit (>0.99) in all countries except Iran (Fig. 2 h) . 200 The maximum percent deviation of predicted data from actual test data for the period 15 May to 201 28 May 2020 for different countries is tabulated in Table 2 . Remarkably, the model was fitted Table 1 . The highest value of the shape factor, 0.152632, was 216 observed to be of China, which clearly presents a sudden abrupt rise in the cumulative cases of 217 infections, as compared to other countries. This was followed by Germany, Japan, Italy, USA, 218 and Canada. Among all the countries, India showed the lowest value of shape factor, i.e., 219 0.018357, which is still at a rapidly increasing pace of cumulative cases. February 2020, which implies the value of maximum spreadability on 18 April 2020. The actual 236 peak spreadability for the USA was identified around 24 April when the cumulative infected population was 925232 [3] . Currently, the infection rate of the virus in the USA is manifesting a 238 declining trend. Based upon the inflection time predicted by the model, the peak of the 239 spreadability for Italy, Canada, Germany and Japan, (Fig. 3 d, e, g, h) found to be very close to corresponding predicted model values. Since these countries had 243 already witnessed the peak spreadability, therefore, currently, spreadability is dropping with 244 time. Surprisingly, Iran (Fig. 3 h) presented an exceptional case of spreadability curve as it had 245 started to present slowing down of spread after attaining a peak, but the outbreak was accelerated 
The spread in India has appeared to be delayed with respect to other counties; hence, the spread 255 rate of the virus is expected to be augmented in coming days, as visualized from increasing 256 predicted trend shown in the Fig. 3 a. The long-term prediction of COVID-19 spread for India, 257 considering a confidence interval of ± 5 % of inflection population (α) is presented in Fig. 4 .
Accounting the data obtained till date, the increasing curve of the cumulative population of 259 infected patients is likely to move towards flattening possibly in October (Fig. 4 a) . Spreadability rate of infection in India may rise more than 9000 new cases per day in June. Accoding to 261 prevailing conditions of May, the inflection time for India is estimated to be 145 days from 15 262 February 2020, indicating the peak of the spreadability to be attained in July. However, it is 263 likely to change due to increase of of virus spreadibility rate and ease on lockdown restrictions in 264 June. The cumulative number of confirmed infected population at inflection is approximated to 265 be more than 5 lakhs. Therefore, it is predicted that in June and July, India is expected to contribution was observed to be stronger, indicating state with higher GDP contribution 296 witnessed a severe outbreak of COVID-19. Further, the shape factor was observed to be 297 negatively correlated with literacy rate, which implies that states having higher literacy rate 298 attained lower shape factor. The literacy rate presented an inverse association with rural to urban 299 ratio, which also negatively correlated with inflection population. It can be suggested that states 300 with lower rural to urban ratio witnessed a higher spread of coronavirus disease.
The data were used in non-normalized form for comparative visualization of the contribution of 302 each factor in confirmed cases. The whole data set formed a relatively spherical data set, and a 303 total of 5 principal components (PC) were found significant (Eigenvalue > 1.0). The data has 304 huge variation due to geographical variation of different states, and the first two PC's were able 305 to explain 54.6% of the total variance in the data. From the PCA biplot (Fig. 5 b) , it can be inferred that states like Maharashtra, Tamil Nadu, Gujarat contributing higher percentages of 307 GDP were more susceptible to coronavirus infection. High GDP contribution can be related to 308 higher economic and commercial activities leading to increase of urbanization and international 309 connectivity. Maharastra is the worst affected state by the outbreak of coronavirus, which could 310 be justified with its high industrialization contributing higher GDP and larger urban population 