Modeling the spread of the Coronavirus
While multiple models exist for predicting the infection rates and plateaus of pandemics, they are generally limited by assumptions of all individuals having an equal probability of transmission. As a result the projections for infection rates tend to focus on larger geographic scales, such as states, resulting in an incomplete picture at a local level.
This talk will introduce statistical modeling concepts relating to prediction of COVID rates at a county level and specifically introduce the notion of spatial correlation, which recognizes that there is a connection between county proximity and amount of social movement between areas.
Dr. Adam Jaeger is an assistant professor in the Department of Mathematics and Statistics at 麻豆传媒映画出品. Previously he was a postdoctoral fellow at Duke University and a visiting assistant professor at Indiana University. While his research interests cover a broad range of statistical concepts, his main focus is on likelihood theory, nonparametric statistics, computational statistics, and spatio-temporal modeling. Dr. Jaeger has worked with data from multiple fields, such as agriculture, imaging, climate, virology, chemistry, and physical therapy.