Open AccessA Neural Network based Model with Lockdown Condition for Checking the Danger Stage Level of COVID-19 Infection Risk
Author(s) -
Shailendra Giri*,
Pradeep K. Yadav,
Harendra Kumar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b4097.079220
Subject(s) - contact tracing , preparedness , quarantine , outbreak , pandemic , china , government (linguistics) , covid-19 , transparency (behavior) , business , computer security , computer science , geography , political science , virology , medicine , infectious disease (medical specialty) , disease , law , linguistics , philosophy , pathology
In December 2019, human history is observing a very strange time fighting an invisible enemy, the novel corona virus (COVID-19). Initially emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries around the world. There is a current worldwide outbreak of a new type of corona virus (COVID-19). Governments are under increased pressure to stop the outbreak spiralling into a global health emergency. A series of mandatory actions have been taken by the municipal and provincial governments supported by the central government, such as measures to restrict travels across cities, contact tracing and case detection, guidance, quarantine and information to the public etc. At this stage, preparedness, transparency and sharing of information are crucial to risk assessments and beginning outbreak control activities. The main objective of this paper is to develop a neural network based algorithm involving lockdown condition between hidden layers for checking the level of COVID-19 infection risk from the pandemic data. The results show that India is in a good condition in comparison of other countries due to timely implementation of the lockdown.