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Predictive Analytics to Combat with COVID-19 using Genome Sequencing

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Document pages: 18 pages

Abstract: The coronavirus outbreak (nCoV) has created an alarming situation at international level. COVID-19 pandemic is now continuing to develop and researchers across the world are working to combat with COVID-19 & to reduce and prevent its spread. Pathological tests are used to detect whether patient is corona positive or not but they are time consuming & also number of test kits & centers are limited. Surprising thing is 60 patients may not have any symptoms but still they are SARS-CoV-2 positive & may work as silent carrier causing outbreak of this disease. And a reason to worry is that yet there is no vaccine & exact drug for this disease. Clinical data related to patient’s medical history, geographical location & symptoms can be analyzed using machine learning techniques to predict clinical outcomes. But as symptoms vary from person-to-person & are common in other diseases like flu, swine flu & pneumonia therefore accuracy of clinical outcomes is questionable. To tackle this problem, this paper proposes a technique to detect presence of SARS-CoV-2 in a person using genome sequencing of virus. This will accurately help to detect presence of virus, to develop targeted therapies & vaccines and also to learn how patient will respond to drugs. This paper summarizes primary survey of existing work & discusses a methodology to detect. Thus proposed methodology can be used by doctors & practitioners as a tool for making ease in decision making.

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