Coronavirus Disease 2019 (COVID-19) pandemic has become the greatest threat to global health in only a matter of months. Iran struggling with COVID-19 coincidence with Nowruz vacations has led to horrendous consequences for both people and the public health workforce. Modeling approaches have been proved to be highly advantageous in taking appropriate actions in the early stages of the pandemic. To this date, no study has been conducted to model the disease to investigate the disease, especially after travel restrictions in Iran. In this study, we exploited the opportunities that Artificial neural networks offer to investigate contributing factors of early-stage coronavirus spread via generating a model to predict daily confirmed cases in Iran. We collected publicly available data of confirmed cases in 24 provinces from April 4, 2020, to May 2, 2020, with a list of explanatory factors. The factors were checked separately for any linear associations and to train and validate a multilayer perceptron network. The accuracy of the models was evaluated, the R2 scores were 0.842 for population distribution, 0.822 for health index, and 0.864 for the population in the provinces. Our results suggest the significant impact of the mentioned factors on disease spread in the time of travel restrictions when the vacation ended. Accordingly, this information can be implicated in assessing the risk of epidemics and future policy makings in this area.