TY - JOUR
T1 - The Number of Confirmed Cases of Covid-19 by using Machine Learning
T2 - Methods and Challenges
AU - Ahmad, Amir
AU - Garhwal, Sunita
AU - Ray, Santosh Kumar
AU - Kumar, Gagan
AU - Malebary, Sharaf Jameel
AU - Barukab, Omar Mohammed
N1 - Publisher Copyright:
© 2020, CIMNE, Barcelona, Spain.
PY - 2021/6
Y1 - 2021/6
N2 - Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
AB - Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
UR - http://www.scopus.com/inward/record.url?scp=85089092313&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089092313&partnerID=8YFLogxK
U2 - 10.1007/s11831-020-09472-8
DO - 10.1007/s11831-020-09472-8
M3 - Article
AN - SCOPUS:85089092313
SN - 1134-3060
VL - 28
SP - 2645
EP - 2653
JO - Archives of Computational Methods in Engineering
JF - Archives of Computational Methods in Engineering
IS - 4
ER -