The COVID-19 pandemic has manifested in a unique outbreak pattern. As the most recent global pandemic, it has significantly affected individuals' daily habits and mobility. These alterations have been location-specific and can be forecasted using geospatial artificial intelligence (GeoAI) modeling. GeoAI and geo-visualization serve as essential tools for gaining clearer insights into the application of spatial phenomena in reality. Additionally, digital twins function effectively as a form of virtual space with practical applications. Today, digital twin (DT) technology receives much attention, but there is still a long way to go in its evolution. Digital twins combine software and human efforts in research, particularly in healthcare. They create virtual replicas of patients for disease modeling, allowing for personalized medicine by simulating disease progression and treatment responses. This enhances predictive accuracy and helps develop tailored therapeutic strategies. This paper aims to detect spatial patterns in the outbreak of coronavirus (COVID-19) using Geospatial Artificial Intelligence (GeoAI) within the framework of digital twins. The main contribution is the application of kernel-based algorithms to the disease distribution pattern. The multilayer perceptron (MLP) considers the relationships of input targets based on a normal distribution, while the radial basis function (RBF) technique considers the assumption of a radial influence zone. The COVID-19 dataset was collected over four months from eight hospitals in Tehran. The interpretation of the results indicates that the RBF network, with an RMSE of 1.77e-08, models the COVID-19 outbreak more accurately than the MLP, which had an RMSE of 0.0037. The application of DT with MLP and RBF represents a powerful approach to modeling and simulating complex systems. Utilizing the artificial neural network (ANN) algorithm within the digital twin framework, health centers can achieve enhanced predictive capabilities and real-time responsiveness, improving treatment processes across various medical domains.