eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2020-07-16 8 2 58 63 10.12691/ajams-8-2-4 AJAMS2020824 article O. C. Asogwa qackasoo@yahoo.com 1 N. M. Eze 2 C. M. Eze 2 C. I. Okonkwo 2 C. U. Onwuamaeze 2 Department of Mathematics, Computer Science, Statistics and Informatics, Alex Ekwueme Federal University Ndufu-Alike Ikwo Department of Statistics, University of Nigeria, Nsukka 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. http://pubs.sciepub.com/ajams/8/2/4/ajams-8-2-4.pdf mean correct classification rate Artificial Neural Networks (ANNs) predictive models COVID-19