Understanding the CoVID-19 pandemic Curve through statistical approach

Ibrar ul Hassan Akhtar

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.06.20055426 doi: medRxiv preprint Recognised, Threatened and Extinct) model also showed capability to discriminate patients among diagnosed and non-diagnosed cases for Italy. Identification of CoVID-19 cases is quickly possible through integration of mobile based survey and artificial intelligence that will reduce disease spread among high risk population 17 . While, the growth models based on logistics, Weibull and Hill equation delivered insight to statistical nature of CoVID-19 epidemiology 18 . Two other models of log-linear for percent change and linear for unit change are developed as data driven approach to understand the CoVID-19 and its gender associations in Hong Kong 19 . An interesting model that has been developed is to carry out global CoVID-19 risks assessment using four Chinese cities modelled fight data (passenger's destinations) from FLIRT database 20 . Some studied targeted environmental parameters of temperature, relative humidity and wind speed to highlight underlying relationships and are statistically (Pearson correlations) linked with CoVID-19 spread and founds temperature to be negligible to moderately relevant 21 .