Modeling of COVID-19 cases of selected states in Nigeria using linear and non-linear prediction models
Babatunde Abdulrauph Olarenwaju
University of Ilorin (Nigeria)
Igboeli Uchenna Harrison
uchenna.igboeli@uniabuja.edu.ng(Nigeria)
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.
Supporting Agencies
Keywords:
ARIMA, ANN, Prediction, PandemicReferences
A. Muhammad, K. Suliman, K. Abeer, B. Nadia, S. Rabeea, COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses, Journal of Advanced Research 24 (2020) 91–98, www.elsevier.com /locate/jare
Google Scholar
T. Tong Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV). Perspectives in Medical Virology. (2006); 16:43-95. DOI: 10.1016/s0168-7069(06)16004-8.
DOI: https://doi.org/10.1016/S0168-7069(06)16004-8
Google Scholar
J. Amzata, K. Aminub, V. Kolob, A. Akinyeleb, J. Ogundairob, M. Dnjibo, Coronavirus outbreak in Nigeria: Burden and socio-medical response during the first 100 days, International Journal of Infectious Diseases 98 (2020), www.elsevier.com/locat e/i jid
DOI: https://doi.org/10.1016/j.ijid.2020.06.067
Google Scholar
NCDC, Nigeria Centre for Disease Control. COVID-19 Outbreak in Nigeria Situation Report; NCDC: Abuja, Nigeria, 2020
Google Scholar
B. Jester, T. Uyeki, D. Jernigan, Readiness for Responding to a Severe Pandemic 100 Years After 1918, American Journal of Epidemiology 187(12), DOI:10.1093/aje/kwy 165
Google Scholar
I. Oladipo, A. Babatunde, Data Mining Classification Techniques for the Diagnosis, Treatment and Management of Diabetes Mellitus: A Review., Proceedings of the 1St International Conference of IEEE Nigeria Computer Chapter In collaboration with Department of Computer Science, University of Ilorin, Ilorin, Nigeria – 2016.
Google Scholar
B. Kaplan CDC Reveals Top 5 Causes of Death in the U.S., https://www.orlando health.com/ content-hub/cdc-reveals-top-5-causes-of-death-in-the-us (accessed 13 August 2020).
Google Scholar
H. Akaike Information Theory and an Extension of the Maximum Likelihood Principle. In: B.N. Petrov and F. Csaki (eds.) 2nd International Symposium on Information Theory: (1793) 267-81 Budapest:
Google Scholar
Akademiai Kiado.
Google Scholar
S. Roy, G.S. Bhunia, P.K. Shit, Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model. Earth Syst. Environ. https://link.springer.com/ article/10.1007/s40808-020-00890-y. doi.org/ 10.1007/s40808-020-00890-y
Google Scholar
Q. Yang, J. Wang, H. Ma, Research on COVID-19 based on ARIMA model—Taking Hubei, China as an example to see the epidemic in Italy, Journal of Infection and Public Health, (2020), Volume 13, Issue 10 Pages 1415-1418
DOI: https://doi.org/10.1016/j.jiph.2020.06.019
Google Scholar
D. Benvenuto, M. Giovanetti, L. Vassallo, Angeletti, M. Ciccozzi, Application of the ARIMA model on the COVID-2019 (2020), Epidemic Dataset, Data in Brief, Volume 29, April 2020, 105340
DOI: https://doi.org/10.1016/j.dib.2020.105340
Google Scholar
A. Oyelola, I. Adeshina, E. Gayawan Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria, (2020). International Journal of Environmental Research and Public Health, 2020, 17, 3054
DOI: https://doi.org/10.3390/ijerph17093054
Google Scholar
O. S. Adams, A. M. Bamanga, U.H. Yahaya, O. R. Akano, Modeling COVID-19 Cases in Nigeria Using Some Selected Count Data Regression Models, (2020), International Journal of Healthcare and Medical Sciences, ISSN(e): 2414-2999, ISSN(p): 2415-5233, Vol. 6, Issue. 4, pp: 64-73, 2020
Google Scholar
Authors
Babatunde Abdulrauph OlarenwajuUniversity of Ilorin Nigeria
Associate Professor of Computer Science, Department of Computer Science, University of Ilorin, Nigeria
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