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Title |
An IoMT based Big Data Framework for COVID-19 Prevention and Detection |
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Author |
Soomaiya Hamid, Narmeen Zakaria Bawany, and Saifullah Adnan |
| Citation |
Vol. 25 No. 9 pp. 105-111 |
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Abstract |
Internet of Medical Things (IoMT) has gained significant attention in the healthcare industry as it is reshaping modern healthcare systems by incorporating technological, economic, and social possibilities. The novel severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as the most critical global challenge for public health. COVID-19 is highly contiguous and spread from person-to-person interaction. Therefore, there is a need to avoid physical interactions between patients and medical health workers. In this regard, an effective and trustworthy daily healthcare service is needed that facilitates remote monitoring of patients on a daily basis. To accomplish this need, we briefly present the role of IoMT-based technologies in COVID-19 and proposed a framework named, cov-AID which remotely monitors and diagnose the disease. The proposed framework encompasses the benefits of IoMT sensors and big data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alert people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. The suggestion and challenges for applying big data to combat COVID-19 are also discussed. |
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Keywords |
IoMT, Big data Framework, Remote Diagnosis, Remote Patient Monitoring, COVID-19 outbreak Detection |
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