Open AccessEffectiveness of Contact Tracing Using KNN for COVID-19
Author(s) -
Maheshwari Venkatasen,
Sandeep Kumar Mathivanan,
M. Prasanna,
Prabhu Jayagopal,
P. Thanapal,
Manivannan Sorakaya Somanathan,
Upendra Babu K,
D. Elangovan
Publication year - 2021
Publication title -
journal of mobile multimedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.229
H-Index - 12
eISSN - 1550-4654
pISSN - 1550-4646
DOI - 10.13052/jmm1550-4646.17415
Subject(s) - contact tracing , covid-19 , tracing , transmission (telecommunications) , quarantine , outbreak , coronavirus , computer science , smallpox , virology , medicine , disease , infectious disease (medical specialty) , telecommunications , vaccination , pathology , operating system
COVID-19 virus started to outbreak in China in the year January 2020. Contact tracing is an open-minded measure of control that applies to an extensive range of transmissible diseases. It is being used to fight infections like SARS, tuberculosis, smallpox, and many sexually transmitted diseases (STDs). From the moment of the lockdown, there have been a great many talks of applications helping to combat the coronavirus. Technical developers bring a solution to this problem by providing tools that help to contain the coronavirus. This kind of application is helpful, but it lacks in accuracy and privacy concerns. COVID-19 virus, irrespective of causes, solution, treatments, clinical signs, and symptoms is discussed in this paper. The main aim of this paper proposes a contact tracing using k-nearest neighbour, which shows the correct prediction of an affected person of COVID-19 based on the distance and also reduces the transmission of disease. It was tested on the WHO dataset obtained the prediction accuracy of which was carried out on clinical and quarantine data. The evaluation result shows that the contact tracing technique’s accuracy has been improved using the proposed algorithm.