Data Visualization of COVID-19 Vaccination Progress and Prediction Using Linear Regression

Authors

  • Hilal H Nuha HUMIC Engineering, School of Computing, Telkom University, Indonesia
  • Ahmad Abo Absa University of Palestine, Palestine, State of

DOI:

https://doi.org/10.15575/join.v7i1.736

Keywords:

COVID19, Data Visualization, Prediction

Abstract

This paper provides a data visualization and analysis of the COVID-19 vaccination program. Important information such as which countries have the highest vaccination rates and numbers. In addition to the types of vaccines used and used by countries in the world, an infographic on the geographic distribution of vaccine use is also shown. To model the obtained data, daily vaccination rates were modeled by linear regression in which five sample countries with different vaccination ranges were processed using data science approach, namely, linear regression. The modeling results show a gradient coefficient that represents an increase in vaccine rates. The prediction results showed that the highest rate of increase in daily vaccination was 1,826,126 additional vaccines per day.

References

R. Lingga and H. H. Nuha, “Accessibility and Response Time Analysis on the COVID19 Website in Indonesia,†2021.

C. Zanettini et al., “Influenza vaccination and COVID19 mortality in the USA,†medRxiv. 2020, doi: 10.1101/2020.06.24.20129817.

Y. Rolland, M. Cesari, J. E. Morley, R. Merchant, and B. Vellas, “COVID19 Vaccination in Frail People. Lots of Hope and Some Questions,†Journal of Nutrition, Health and Aging. 2021, doi: 10.1007/s12603-021-1591-9.

A. D. Praticò and M. Ruggieri, “In Covid19-era, is it time for a mandatory Flu-vaccination for children?,†Science (80-. )., 2020.

M. C. Arokiaraj, “Correlation of Influenza Vaccination and the COVID-19 Severity,†SSRN Electron. J., 2020, doi: 10.2139/ssrn.3572814.

M. Yousef, L. Showe, and I. Ben Shlomo, “Machine Learning Analysis of National Vaccination Schedules and Rates of Compliance Reveals Correlation with COVID19 Mortality,†Int. J. Comput. Biol., vol. 9, pp. 1–6, 2020.

N. Sutaria, “COVID-19 Vaccination Progress Analysis through Data Storytelling,†2021. https://towardsdatascience.com/covid-19-vaccination-progress-analysis-around-the-world-736d7e57f198 (accessed Mar. 19, 2021).

“COVID-19 World Vaccination Progress.†https://www.kaggle.com/gpreda/covid-world-vaccination-progress/ (accessed Mar. 29, 2021).

G. Press, “Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says,†Forbes Tech, 2016.

B. Liu et al., “A multi tone modeling for seismic data compression,†in SEG Technical Program Expanded Abstracts, 2019, pp. 263–267.

B. Liu, M. Mohandes, H. Nuha, M. Deriche, F. Fekri, and J. H. McClellan, “A Multitone Model-Based Seismic Data Compression,†IEEE Trans. Syst. Man, Cybern. Syst., 2021, doi: 10.1109/tsmc.2021.3077490.

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Published

2022-06-30

Issue

Vol. 7 No. 1 (2022)

Section

Article

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