American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2020, 8(2), 58-63
DOI: 10.12691/ajams-8-2-4
Open AccessArticle

On the Modeling of the Effects of COVID-19 Outbreak on the Welfare of Nigerian Citizens, Using Network Model

O. C. Asogwa1, , N. M. Eze2, C. M. Eze2, C. I. Okonkwo1 and C. U. Onwuamaeze2

1Department of Mathematics, Computer Science, Statistics and Informatics, Alex Ekwueme Federal University Ndufu-Alike Ikwo

2Department of Statistics, University of Nigeria, Nsukka

Pub. Date: July 16, 2020

Cite this paper:
O. C. Asogwa, N. M. Eze, C. M. Eze, C. I. Okonkwo and C. U. Onwuamaeze. On the Modeling of the Effects of COVID-19 Outbreak on the Welfare of Nigerian Citizens, Using Network Model. American Journal of Applied Mathematics and Statistics. 2020; 8(2):58-63. doi: 10.12691/ajams-8-2-4


A multilayer perception algorithm based model was established in this research. The result from the test data evaluation showed that the established Artificial Neural Network model was able to correctly predict and classify the effects of COVID-19 outbreak on the well-being of Nigerian citizens with Mean Correct Classification Rate () of 98.05%. The value of the of the model which was classified as good (88.23%), also aligned with the result obtained from the Mean Correct classification rate. The architecture model also indicated a very high sensitivity and a low specificity values respectively. The study was able to show that some factors like economy, farming activities, religious activities, education, etc. were negatively affected whereas crime rates, unwanted pregnancy, relationship between parents and their children were positively influenced during the lockdown period of coronavirus pandemic outbreak in Nigeria.

mean correct classification rate Artificial Neural Networks (ANNs) predictive models COVID-19

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[3]  Nigeria population. (2020). Demographics, Maps and Graphs _1586361654411.pdf-adobe reader.
[5]  Lewis, M. Paul; Gary F. Simons; & Charles D. Fennig, eds. (2016). "Nigeria". Ethnologue: Languages of the World (19th ed.). Dallas, Texas: SIL International Publications.
[6]  https://en.Nigeria/Culture%20of%20Nigeria%20-%20Wikipedia.htm#cite_ref-1.
[9]  Manufacturing sector report, (2015). Manufacturing in Africa. KPMG.
[12]  FIRST CASE OF CORONA VIRUS DISEASE CONFIRMED IN NIGERIA, (NCDC-2020)". Nigeria Centre for Disease Control. 28 February 2020. Retrieved 10 March 2020
[13]  Becerikli, Y., Konar, A. F., & Samad, T. (2003). Intelligent optimal control with dynamic neural networks. Neural Netw. 16: 251-259.
[14]  Dassen, W. R., Mulleneers, R. G., Den, Dulk K., Smeets, J. R., Cruz, F., Penn, O. C., & Wellens, H. J. (1990). An artificial neural network to localize atrioventricular accessory pathways in patients suffering from the Wolff-Parkinson-White syndrome. Pacing Clin. Electrophysiol.13, (12): 1792-1796.
[15]  Kao, J. J, & Huang, S. S. (2000). Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data. J. Air Waste Manag. Assoc. 50: 219-226.
[16]  Mohamed E. I., Linder, R., Perriello, G., Di, Daniele. N., Poppl, S. J. & De, Lorenzo. A. (2002). Predicting Type 2 diabetes using an electronic nose based artificial neural network analysis. Diabetes Nutr. Metab. 15: 215-221.
[17]  Augusteijn, M. F, & Shaw, K. A. (2002) Constructing a query-able radial basis function artificial neural network. Int. J. Neural Syst. 12: 159-175.
[18]  Castellaro, C., Favaro, G., Castellaro, A., Casagrande, A., Castellaro, S., Puthenparampil, D. V., & Salimbeni, C. F. (2002) An artificial intelligence approach to classify and analyses EEG traces. Neurophysiol. Clin. 32: 193-214.
[19]  Cimander, C., Bachinger, T., & Mandenius, C. F. (2003) Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system. J. Biotechnol. 103: 237-248
[20]  Yen, G.G. & Meesad, P. (2001). Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm. Int. J. Neural Syst. 11: 427-443.
[21]  Z. Liu, P. M., O. Seydi, & G. Webb, (2020). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data.
[22]  Al-Qaness, M. A. A.; Ewees, A. A.; Fan, H.; & El Aziz, M., (2020) Optimization Method for Forecasting Confirmed Cases of COVID-19 in China. J Clin. Med. 9, (3).
[23]  Jung, S. M., Akhmetzhanov, A. R., Hayashi, K., Linton, N. M., Yang, Y., Yuan, B., Kobayashi, T., Kinoshita, R. & Nishiura, H. (2020). Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases. Journal of Clinical Medicine. 9, (2).
[24]  Fan, C.; Liu, L.; Guo, W.; Yang, A.; Ye, C.; Jilili, M.; Ren, M.; Xu, P.; Long, H.; & Wang, Y., (2020). Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study. Int. J. Environ. Res. Public Health. 17, (5).
[25]  Zixin Hu, Q. G., Shudi Li, Li Jin & Momiao Xiong. (2020). Artificial Intelligence Forecasting of Covid-19 in China.
[26]  Guo, Q.; Li, M.; Wang, C.; Wang, P.; Fang, Z.; tan, J.; Wu, S.; Xiao, Y. & Zhu, H. (2020). Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm.
[27]  Liu, T., Hu, J., Xiao, J., He, G., Kang, M., Rong, Z., Lin, L., Zhong, H., Huang, Q., Deng, A., Zeng, W., Tan, X., Zeng, S., Zhu, Z., Li, J., Gong, D., Wan, D., Chen, S., Guo, L., Li, Y., Sun, L., Liang, W., Song, T., He, J.,& Ma, W. (2020) Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China.
[28]  Metsky, H. C., Freije, C. A., Kosoko-Thoroddsen, T. S. F., Sabeti, P. C. & Myhrvold, C. (2020) CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design.
[29]  Ming, W.-K., Huang, J. & Zhang, C. J. P. (2020). Breaking down of healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China.
[31]  WMHC. (2020) Wuhan Municipal Health and Health Commission’s Briefing on the Current Pneumonia Epidemic Situation in Our City. 2020.
[32]  Sasmita, P. A., Sha M., Yu-Ju W., Yu-Ping M., Rui-Xue Y., Qing-Zhi W., Chang S., Sean S., Scott R., Hein R., and Huan Z. (2020). Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infectious Diseases of Poverty. 9, (29).
[33]  CDC. (2019) Novel coronavirus, Wuhan, China.
[34]  Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl. J. Med.
[35]  WHO. Novel Coronavirus-China. (2020). china/en/. Accessed 1 Feb 2020.
[36]  Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. (2019). A novel coronavirus from patients with pneumonia in China. N Engl. J. Med.
[37]  Anders, U. (1996) Model selection in neural networks, ZEW Discussion Papers 96-21.
[38]  O. C. Asogwa and A. V. Oladugba (2015). Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs). American Journal of Applied Mathematics and Statistics.3, (4): 151-155.
[39]  Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function. Journal Mathematics of Control, Signals, and Systems, 2, (4): 303-314.
[40]  Hornik, K., Stinchcombe, M. & White, H. (1989). “Multilayer feed forward networks are universal approximators,” Neural Networks. 2, (5): 359-366.
[41]  Adefowoju, B. S. & Osofisan, A. O. (2004). Cocoa Production Forecasting Using Artificial Neural Networks. International Centre for Mathematics and Computer Science Nigeria. ICMCS 117-136.
[42]  El-Sebakhy, E. A., Hadi, A. S. & Faisal, K. A. (2007). Iterative Least Squares Functional Networks Classifier. IEEE Transactions on Neural Networks. 18, (3): 844-850.
[43]  Ritchie, S. G & Oh, C. (2007). Recognizing vehicle classification information from blade sensor signature, Pattern Recognition Letters. 28, (9): 1041-1049.
[44]  Oladokun, V. O., Charles-Owaba. O. E. & Adebanjo, O. E. (2008) Predicting Student’s Academic performance using artificial neural network: A case study of an engineering course. The pacific Journal of Science and Technology. 9, (1): 72-79.
[45]  Thipsuda, W. & Pusadee, S. (2010) A comparison of classical Discriminant Analysis and Artificial Neural Network in predicting student graduation outcomes. Proceedings of the Second International Conference of knowledge and Smart technologies: 24-25.