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Bio-Surveillance of Covid-19 Data using Statistical Process Control

Presented by:
Payton Miloser (Grand Valley State University)

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's I. Using Moran's I this has 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's I and Statistical Process Control to predict outbreaks.