SARS-CoV-2 genome datasets analytics for informed infectious disease surveillance

Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood emotional complications ensuing from lockdowns commercial inactivity, resulted in governments loosening restrictions. These concerns were further exacerbated by the absence vaccines drugs to combat disease, with fear that next wave would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, reduce spread community cross-border infections offer informed medical decisions. Methods: Publicly available datasets coronavirus cases around globe deposited between December, 2019 March 15, 2021 retrieved GISAID EpiFluTM processed. Also GISAID data on different SARS-CoV-2 variant types since inception pandemic. Results: Epidemiological analysis offered interesting statistics for understanding demography helped elucidation local foreign transmission through a history contact travels. Results genome pattern visualization cognitive knowledge mining revealed emergence high intra-country viral sub-strains localized routes traceable immediate countries, enhanced tracing protocol. Variant indicates increased need continuous monitoring variants. A collaborative Internet Health Things (IoHT) framework was finally proposed impact system, robust intelligent support modelling, characterizing, diagnosing real-time infectious diseases. Conclusions: Localizing crucial emerging situations will real-time/updated case definitions suspected probable cases. The IoHT this paper assist early syndromic assessments diseases healthcare/medical countermeasures as well useful strategies making policy decisions drive cost effective, smart system.