In recent years, Artificial Neural Networks (ANN) was widely implemented for developing predictive and estimation models to estimate the needed parameters. As the Coronavirus disease 2019 (COVID-19) case numbers are rising internationally as uncontrolled outbreaks, it is important to better understand what factors promote the super spreading events. In this paper, the use of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) of ANN for COVID-19 spread and death contributing factors in America was described. A comparison was made by using a dataset of COVID-19 cases and deaths reported from 49 states in America during April 2020. Seven covariates used in the network which are High Temperature, Low Temperature, Average Temperature, Population, Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. However, the performance of MLP and RBF networks may be evaluated relatively similar. It was found that both MLP and RBF proved that the Population, Percentage of cases over population, and Total cases are the most contributing factors towards COVID-19 spread and death in America particularly.