Automatic Surveillance of Pandemics Using Big Data and Text Mining

COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with s...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2021-01, Vol.68 (1), p.303-317
Main Authors: Alharbi, Abdullah, Alosaimi, Wael, Irfan Uddin, M.
Format: Article
Language:English
Subjects:
Algorithms
Coronaviruses
COVID-19
Data mining
Disease control
Disease transmission
Machine learning
Recurrent neural networks
Signs and symptoms
Surveillance
Viral diseases
Viruses
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Summary:COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with symptoms of COVID-19 are tested using medical testing kits; these tests must be conducted by healthcare professionals. However, the testing process is expensive and time-consuming. There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken. This paper introduces a novel technique based on deep learning (DL) that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19. The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high; clinical tests should then be used to test and identify infected individuals. The system proposed here uses recurrent neural networks (RNN) and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus. The results demonstrate that RNN can conduct this analysis more accurately than other machine learning (ML) algorithms.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.016230