Journal ID : TRKU-13-08-2020-10998
[This article belongs to Volume - 62, Issue - 08]
Total View : 346

Title : Predicting Diffusion Dynamics Of The Coronavirus In Nigeria Through Ties-Strength Threshold On A Cascading SI-Graph

Abstract :

Graphs have quickly become dominant life-form of tasks in a society with human actors as nodes. Actors’ ties bind them to each other – forming a social graph that can be analyzed. The actor’s (nodal) feats via interaction, impacts on the entire graph as a global structure. These feats evolves the graph, orchestrating a convergence pattern to predict the expected number that adopt/reject an innovation as its outcome. The advent of the fast-paced contagion (corona-virus) covid-19 via the socio-economic strata of Nigeria, has outcome an adverse malignant effect that is today, difficult to treat. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%. This value will continue to decrease in terms of percentage of confirmed cases and death when compared to other nations if the health and safety measured are still being implemented by the populace

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