Modeling of COVID-19 cases of selected states in Nigeria using linear and non-linear prediction models


Abstract

COVID-19 has stamped an indelible mark  in the history of humanity as one of the recorded deadly virus that has wiped out millions of lives on planet earth many whose exact cause of death cannot be account for due to lack of knowledge. It has become a household name in every nook and cranny from developed to the underdeveloped nations of the world. Most of the prominent signs of COVID-19 like fever, cough, difficulty in breathing and accessional muscle pain can also resemble those of many other notable diseases thereby making it highly necessary to undergo a diagnostic test to be able to categorically identify COVID-19 patients. The use of medical diagnostic tests can also help determine patients who have recovered from COVID-19. Various studies abound with researchers trying to predict and even forecast the level of damage and disruption of economic activities this may have brought to almost every nation of the world. This research attempts to find out the nature of the spread of the virus using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). The essence is to ascertain the exact model to use in forecasting the future occurrence of the pandemic especially at this stage where the second wave of the pandemic is in view. The study found that both linear and nonlinear predictions models can fit the trend of the virus in Nigeria with ARIMA producing results of over 97% on a 120-day period while ANN produced results of about 98.01% in some states. We conclude that future waves of the virus in addition to other epidemics of this nature can be predicted with high degree of accuracy with ARIMA or ANN.


Keywords

ARIMA, ANN, Prediction, Pandemic

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Published : 2020-12-30


Abdulrauph Olarenwaju, B., & Harrison, I. U. (2020). Modeling of COVID-19 cases of selected states in Nigeria using linear and non-linear prediction models. Journal of Computer Sciences Institute, 17, 390-395. https://doi.org/10.35784/jcsi.2368

Babatunde Abdulrauph Olarenwaju 
University of Ilorin  Nigeria

Associate Professor of Computer Science, Department of Computer Science, University of Ilorin, Nigeria


Igboeli Uchenna Harrison  uchenna.igboeli@uniabuja.edu.ng