THE ROLE OF HUMAN POPULATION DENSITY AND THE ELEMENTS OF WEATHER IN THE SPREAD OF COVID-19 IN NIGERIA: A NEGATIVE BINOMIAL REGRESSION MODEL APPROACH

This novel study was undertaken to determine the role of human population density and the elements of weather, namely temperature, rainfall and humidity in the spread of Coronavirus 2019 in Nigeria. Secondary data from Nigerian Center for Disease and Control and Climate-data were used. The number of confirmed COVID-19 cases, which is the dependent variable is a non-negative discrete random variable. This suggested the use of the Poisson regression and the negative binomial regression to model the counts as a function of the covariates. The best distribution and regression model were chosen based on the Chi-square goodness of fit, Bayesian Information Criteria, Akaike Information Criteria, and the ability of the data to satisfy the assumptions of the models. The negative binomial regression model was identified as the best model that fits the data since the dependent variable is over dispersed and follows a negative binomial distribution. The result revealed that temperature and human population density are statistically significant at 5% level of significance in explaining the variations in the number of COVID-19 confirmed cases in Nigeria. The sign and value of the exponent of the regression parameters showed that a unit increase in temperature decreases the number of COVID-19 confirmed cases in Nigeria by 30.1% and increases it by 0.1% in a unit increase of the human population density. This implies that incidence of COVID-19 is reduced by increasing the temperature but increased, in larger gatherings. We found that the COVID-19 virus would not spread quickly in a hot environment; it prefers a cold environment to be able to infect densely populated areas.