Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria

  • Arnold Adimabua Ojugo Federal University of Petroleum Resources Effurun (NG)
  • Andrew Okonji Eboka Department of Network Computing, Coventry University, United Kingdom (GB)
Keywords: Coronavirus, COVID-19, graph-model, SIS, SIR, epidemiology, pandemic

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Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at ‘aged’ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. 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%.


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How to Cite
A. A. Ojugo and A. O. Eboka, “Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria”, J. Appl. Sci. Eng. Technol. Educ., vol. 3, no. 2, pp. 135-144, Jul. 2020.