COVID-19 Deaths Previsions With Deep Learning Sequence Prediction: Bacille Calmette-Guérin (BCG) and Tuberculosis Track

Heni Bouhamed

Source Title: International Journal of Big Data and Analytics in Healthcare (IJBDAH)5(2)

ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781799808381|DOI: 10.4018/IJBDAH.20200701.oa1

MLA

Bouhamed, Heni. "COVID-19 Deaths Previsions With Deep Learning Sequence Prediction: Bacille Calmette-Guérin (BCG) and Tuberculosis Track." IJBDAH vol.5, no.2 2020: pp.65-77. http://doi.org/10.4018/IJBDAH.20200701.oa1

APA

Bouhamed, H. (2020). COVID-19 Deaths Previsions With Deep Learning Sequence Prediction: Bacille Calmette-Guérin (BCG) and Tuberculosis Track. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 5(2), 65-77. http://doi.org/10.4018/IJBDAH.20200701.oa1

Chicago

Bouhamed, Heni. "COVID-19 Deaths Previsions With Deep Learning Sequence Prediction: Bacille Calmette-Guérin (BCG) and Tuberculosis Track," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 5, no.2: 65-77. http://doi.org/10.4018/IJBDAH.20200701.oa1

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Abstract

In this study, the authors use deep learning sequence prediction models for the continuous monitoring of the epidemic while considering the potential impacts of Bacille Calmette-Guérin (BCG) vaccination and tuberculosis (TB) infection rates in populations. Three models were built based on the epidemic data evolution in several countries between the date of their first case and April 1, 2020. The data was based on 14 variables for cases prediction, 15 variables for recoveries prediction, and 16 variables for deaths prediction. Prevision results were very promising, and the suspicions on the BCG vaccination and TB infections rates' implications turned out to be warranted. The model can evolve by continuously updating and enriching data, adding the experiences of all affected countries.