Data Mining of COVID-19 Cases and Food Security in Nigeria

Abstract

The poor public health sector, inadequate welfare programme, state of insecurity coupled with the increasing COVID-19 cases in Nigeria had affected the wellbeing of her citizens.  During the outbreak of COVID-19 pandemic, people living in poverty did not have welfare relief that could help them cope with the economic hardship at the time. In this paper, Data Mining of COVID-19 Cases and Food Security in Nigeria is examined using the data from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 7^th December 2020 and data on National Food Prices from National Bureau of Statistics. The data were subjected to descriptive and inferential statistics. Generalized Negative Poisson regression was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and the result revealed that admitted and discharged cases had negative and inverse relationship with COVID-19 related deaths in the country while increase in laboratory confirmed cases   had a positive and significant effect on the number of deaths. The pandemic had a negative impact on food prices thereby affecting food security of citizens.