Abstract

Background: An epidemic disease is contagious and have the capability of spreading into entire nation if timely adequate measures are not taken to curb the epidemic. Coronavirus outbreak from Wuhan in South China is engulfing approximately entire world within nearly four month of time span. Till date nearly 10 lakh people are infected and around 50 thousand died due to this disease around the world. In this article we are going to represent the graphical trends of infected cases around the WHO six zone, calculation of Basic Reproduction Number, Using Machine Learning tried to find out the near future scenario.

Method: we collected number of infectious cases data from various official source. We complied the data according to the need of our study and performed data validation from IBM SPSS software. Further, we plotted various graph using Microsoft Excel tools. Studied the nature of graphical trend and discussed. Using RStudio we calculated the Basic reproduction Number of WHO zones and countries. Lastly, we used Machine learning in forecasting the near future trend of infected number of cases in near future.

Findings: Our finding in the graphical trend is we found that except China all other studied areas have Exponential phase of increase in number of new cases. Whereas, china is in plateau phase. Basic Reproduction Number is highest for USA and Regions of Americas 2.11, 2.08 respectively. Predicted mean infected cases of Italy is 375375.08 followed by Eastern Mediterranean Region 123044.04 by 4 April 2020. Thses results are adding an extra information add on for the policy makers and healthcare providers to act accordingly and also adding new dimension to the existing Knowledge data base.

Interpretaion: our study shows that almost all WHO zone is facing an exponential increase in infected cases. As well as its trend is increasing, thus it the time to act more aggresivley and work collabroatevely to curb the disease and stop the spread.

Keywords: Trend; Pandemic; Machine Learning; Basic Reproduction Number; Forecast; Poisson Regression; COVID-19; SARS COV

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Citation

Citation: Keshav Kumar., et al. “Graphical Trends and Machine Learning Forecast of COVID19 Around the Globe". Acta Scientific Dental Sciences 4.10 (2020): .

Copyright

Copyright: © 2020 Keshav Kumar., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.