An Evaluation of the Frameworks for Predicting COVID-19 in Nigeria Using Time Series Data Analytics Model

Udanor, C. N.; Eneh, A. H.; Orim, S. M. I.

When the novel coronavirus, COVID-19 broke out in Wuhan China, not many in this generation knew how much impact it was going to have on our daily lives. The pandemic has resulted in the loss of thousands of lives, the collapse of businesses, indefinite closure of schools, and other activities resulting to the need for a ‘new normal’. Virologists, epidemiologists, and all other scientists are working round the clock to bring a stop to this ravaging disease. The virus which exhibits severe acute respiratory syndrome was first detected in Wuhan China towards the end of 2019, with a fatality rate of 2 to 3%. By the first week of May 2020, the infection rate stood at over 3.4 million confirmed cases, with more than 238,000 deaths across 215 countries. Many virologists and epidemiologists have come to trust computer-based approaches in finding solutions to diseases of this kind. This study seeks to evaluate the appropriateness and effectiveness of relevant models e.g. SIR (Susceptible, Infective, Recovered) model in predicting the spread of nCov-19 using live time-series data while making Nigeria a case study. Results from the prediction suggest that, like many other countries, Nigeria has entered the exponential state of the pandemics. Although the pandemic was well managed at the onset, the results also depict that full relaxation of the lockdown will raise the moderate transmission rate (Ro) value of 1.22. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.