Data-Fusion for Epidemiological Analysis of Covid-19 Variants in UAE

Since December 2019, a new pandemic has appeared causing considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to within short time. virus was further observed be spreading rapidly mutating at fast pace, with over 5,775 distinct variations of the globally (at time submitting this paper). Extensive research been ongoing worldwide in order get better understanding its behaviour, influence more importantly, ways for reducing Data analytics playing pivotal role obtain valuable insights into fighting against spread infection. However, is resource intensive, making it difficult observe quickly identify impact mutations. Factors such as or virulence could explain three months delay revealing variant UK. This paper presents an extensive correlation analysis effect caused by different strains, their on population across diverse factors, propagation fatality rates, during peak pandemic, focus two major countries Middle East, United Arab Emirate (UAE) Kingdom Saudi Arabia (KSA). aims investigate epidemiological behaviour Coronavirus’ genomic variants UAE, compared KSA, where carried out number cases, deaths statistical deviations. results highlight very interesting Covid-19 both countries, which lead important actions taken minimize wider public health, possibly saving lives, economy. For instance, our method identifies potential between spike per case 5.5 UAE March 24th, emergence Coronavirus (G0_c, G0_e1 G0_e2). Our proposed methodology can instrumental identifying classifying earlier, predicting foreseeable mutations through pattern analysis, hence creating proactive measures control spread, recent variant, recently discovered