The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death MESHD cases in Nigeria using six estimation methods i.e. the attack rate TRANS, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity SERO analysis with respect to the mean generation time is used to quantify the associated reproduction number TRANS uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number TRANS estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number TRANS estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number TRANS estimate is different from the default reproduction number TRANS estimate. Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death MESHD and cumulative confirmed cases TRANS are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.