Agunloye, Emmanuel; Usman, Mohammed A; (2021) Predictive Modelling of Covid-19 Pandemic: World Perspective. In: Proceedings of the 15th Annual (1st Virtual) UNILAG Research Conference and Fair. (pp. pp. 1-17). UNILAG: Lagos, Nigeria.

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Abstract

COVID-19, a disease caused by coronavirus, is a global pandemic currently ravaging the world. From tens of cases reported in January 2020, the disease as of August 2020 has infected over 16 million people worldwide, thus becoming a concern to everyone. Modelling can be used to describe the pandemic and assess the effectiveness of various control measures. This paper presents results from a model developed to predict the time evolution of total confirmed cases of COVID-19. The model, which is an exponential equation, is derived from the balance equation modelling, where the time evolution of cases is the accumulation term. While COVID-19 carriers’ cross-border migration can affect the cases within a society, the developed model considers a closed border scenario and mimics a batch system. Therefore, confirmed cases can only be affected by transmission within a society as well as death or recovery rate of infected cases. While death or recovery occurs days after, a carrier can begin transmission as soon as coming in contact with the disease. China, the country where the disease was first reported, did not record any death or recovery from COVIDd-19 until midJanuary 2020 (Ravelo and Jerving, 2020). Neglecting death and recovery terms, the model resulted in an exponential equation. Some of the COVID-19 data reported in China from 01 – 11 January 2020 were used to obtain the exponential rate constant as 0.1706

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