This paper is published in Volume-7, Issue-2, 2021
Area
Information Technology
Author
Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar
Org/Univ
Priyadarshini College of Engineering, Nagpur, Maharashtra, India
Pub. Date
28 April, 2021
Paper ID
V7I2-1389
Publisher
Keywords
Covid-19, Prediction, Mobility, Lock-Down, Case

Citationsacebook

IEEE
Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar. A review on the impact of mobility patterns and prediction on Covid-19 rates, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar (2021). A review on the impact of mobility patterns and prediction on Covid-19 rates. International Journal of Advance Research, Ideas and Innovations in Technology, 7(2) www.IJARIIT.com.

MLA
Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar. "A review on the impact of mobility patterns and prediction on Covid-19 rates." International Journal of Advance Research, Ideas and Innovations in Technology 7.2 (2021). www.IJARIIT.com.

Abstract

After the outbreak of COVID-19 several prediction models are being used by authorities and officials all over the world to implement appropriate control measures and to make well-informed decisions. Due to an enormous level of ambiguity and shortage of crucial data, conventional models have shown low efficiency for long-term prediction. A coronavirus is a contagious disease that is resulting in the massive growth of COVID-19 cases, therefore, we have used human mobility patterns as the effective factor to take preventive measures to thereby stop the outbreak. In this model, we have used methods such as exponential growth, and prophet models. We here demonstrate the impact of changes in mobility patterns by binding the data in Data Science to efficiently trace the disease, plan strategies, methods and foretell the future growth of the pandemic.