BIOPAK Flasher: Epidemic disease monitoring and detection in Pakistan using text mining

• URL: http://arxiv.org/abs/2106.06720v1
• Date: Sat, 12 Jun 2021 08:55:40 GMT
• Title: BIOPAK Flasher: Epidemic disease monitoring and detection in Pakistan using text mining
• Authors: Muhammad Nasir, Maheen Bakhtyar, Junaid Baber, Sadia Lakho, Bilal Ahmed, Waheed Noor
• Abstract summary: Early detection of outbreaks plays an important role here. Few early warning outbreak systems exist with some limitation of linguistic (Urdu) and covering areas. The aim is to procure information from Pakistan's English and Urdu news channels and then investigate process, integrate, and visualize the disease epidemic.
• Score: 0.5888325379746631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Infectious disease outbreak has a significant impact on morbidity, mortality and can cause economic instability of many countries. As global trade is growing, goods and individuals are expected to travel across the border, an infected epidemic area carrier can pose a great danger to his hostile. If a disease outbreak is recognized promptly, then commercial products and travelers (traders/visitors) will be effectively vaccinated, and therefore the disease stopped. Early detection of outbreaks plays an important role here, and beware of the rapid implementation of control measures by citizens, public health organizations, and government. Many indicators have valuable information, such as online news sources (RSS) and social media sources (Twitter, Facebook) that can be used, but are unstructured and bulky, to extract information about disease outbreaks. Few early warning outbreak systems exist with some limitation of linguistic (Urdu) and covering areas (Pakistan). In Pakistan, few channels are published the outbreak news in Urdu or English. The aim is to procure information from Pakistan's English and Urdu news channels and then investigate process, integrate, and visualize the disease epidemic. Urdu ontology is not existed before to match extracted diseases, so we also build that ontology of disease.

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