The ability to predict virus outbreaks is important for assessing the spread of the virus and handling the impact of the spread on the population. The COVID-19 pandemic has provided data that can be studied at the county level that contributes to the knowledge and research surrounding the eradication of the virus; at the county level, the ability to track the spatial dependence of COVID-19 spread between counties across the United States can be done using the geospatial autocorrelation statistic, Moran\u27s I. Using Moran\u27s I we have been able to track the spatial dependence of the COVID-19 cases throughout the pandemic and visualize spikes in Coronavirus case rates to predict outbreaks. This study will present methods for tracking incident type data using Moran\u27s I and statistical process control techniques to predict outbreaks