@inproceedings{657c9e36f2d84b05bb08703d6f6fc3a7,
title = "The study of the effect of the data collected during vaccination period on the prediction of the number of Covid-19 cases",
abstract = "Coronavirus disease (Covid-19) is a serious health problem for the world. Most of the countries are affected by this infectious disease. Many countries have started vaccination against Covid-19. The number of confirmed cases every day changes rapidly. Public health planners want to know these numbers in advance to arrange health facilities accordingly. Many machine learning models have been developed for the prediction of the number of Covid-infected people. The accuracy of these models depends upon the training data. Data collected during the period when there is no vaccination and data collected during the vaccination period have different properties. The models trained on different datasets perform differently. In this paper, we study the effect of the data collected during the vaccination period. The study will be helpful in generating more accurate prediction models for the vaccination period.",
keywords = "Covid-19, confirmed cases, regression methods, vaccination",
author = "Amir Ahmad and Ray, {Santosh Kumar} and Kumar, {Ch Aswani} and Apurva Anand and Cheratta, {Muhsin Jabbar}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021 ; Conference date: 11-11-2021 Through 13-11-2021",
year = "2021",
doi = "10.1109/I-SMAC52330.2021.9641006",
language = "English",
series = "Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "286--289",
booktitle = "Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021",
}
