COVID19 is a new pandemic triggered by extreme acute respiratory coronavirus syndrome 2 (SARS-CoV-2) and spreads rapidly from person to person (Ceylan, 2020) . This pandemic is the most notable world crisis since the Second World War. According to literature, the outbreak originated from Wuhan, China, in late 2019 and has triggered a global economic crisis whose impact will be felt for years to come ; WHO, 2020). On 30th January 2020, the World Health Organization (WHO) declared the outbreak as a Public Health Emergency of International concern (International Health Regulations, 2020; World Health Organization 2020).
There has been about 8, 061, 550 COVID-19 incidents registered in over 200 republics and J o u r n a l P r e -p r o o f regions which had brought about around 440,290 demises as of 18th June 2020 (NCDC Nigeria, 2020). The disease is mainly transmitted through nearby interaction, mostly by tiny beads formed through coughing, sneezing, or speaking (World Health Organization, 2020; Centers for Disease Control and Prevention, 2020; European Centre for Disease Prevention and Control, 2020). Also, individuals could often turn out to be contaminated through being in contact with an affected exterior (World Health Organization, 2020; Centers for Disease Control and Prevention, 2020).
Nigeria announced the Sub-Sahara Africa's first confirmed case of COVID-19 disease on Friday 28th January 2020 at around 1 am. This confirmation led to the activation of the country's National Coronavirus Emergency Operation centre (Adepoju, 2020 This study aims to predict the prevalence of COVID-19 in Nigeria using a linear regression model. Also, to measure the impact of travelling history and contact on COVID-19 confirmed cases.
We consider the general linear regression model:
where y is an 1 × n vector of response variable, X is a known p n × full rank matrix of predictor or explanatory variables, β is an (Gujarati, 1995) . In this study, data was extracted from the NCDC website https://ncdc.gov.ng/. The dataset was collected in an excel file and analysed with the GRETL software. The variables of interest include confirmed cases as the response variable, travelling history and contact as the regressors. The regression model in this study is as follows:
where y represents COVID-19 confirmed cases in Nigeria, X 1 represents the travelling history before and after lockdown and X 2 represents the number of contacts made by a COVID-19 patient. We conducted a correlation analysis to investigate the relationship between the regressors and the response variable. The results in Table 1 shows that a strong positive relationship exists between the confirmed cases and contact. A low and moderately high positive relationship exists between confirmed cases and travelling history; contact and travelling history, respectively. The descriptive statistics of the variables of interest are available in Table 2 Table 3 . 
The Jarque-Bera test in Table 3 shows that the error term is normally distributed. From Table 3 , we observed that the contact have a positive influence on COVID-19 confirmed cases as expected while travelling history have a negative impact as expected. The introduction of travelling lockdown leads to about 4.8% reduction in the number of COVID-19 cases that could have happened. We observed that the dataset on travelling history became constant from April 14, 2020, when the travelling restriction was placed on both local and international flights by the Federal government of Nigeria. We illustrate this graphically in Figure 1 . Figure 1 shows that there is a daily rise in travel history up to the point where a ban was placed on all travels which are responsible for the stability seen in travel history over a period. Because the first case of COVID-19 in Nigeria was from an Italian that came into the country on February 25 2020, this necessitates us to only run a regression model for the dataset from March 31, 2020, to April 13 2020. The regression model for the reduced data set is available in Table 4 . J o u r n a l P r e -p r o o f in Table 4 shows that the model does not exhibit multicollinearity problem since VIF is less than 10 . We further examined if the model has an error term problem. The Jarque-Bera test shows that their error terms come from a normal distribution. The white test and the Durbin-Watson test shows that the error terms are not correlated and possesses constant variance. All these diagnostic checks further strengthen the fact that the performance of OLS estimator in this study is efficient. Figure 3 shows that the predicted value and the actual values are close. From the Table 5 , we observed that the actual confirmed cases fall in the prediction interval. The predicted single estimates are also in agreement with the actual values except on few cases. 
Statistical methods and the time series models have been adopted in previous studies to predict epidemic cases. The linear regression model is an essential analytical tool for prediction. In this estimator was used to estimate the parameters of the model. We carry out diagnostic checks and found out the model fitted well to the dataset. We compared the actual values with the predicted values from April 5 to April 13, 2020, and observed the predictions were very close. We found that travelling history and contacts increase people chances of being infected with COVID-19 by 85% and 88% respectively. In conclusion, the government should enforce the right policy for the containment of COVID-19.