Pandemic vs epidemic: Applications of spatial statistics for disease surveillance

Abstract: Spatial data analysis concerns data that are correlated by location, and relies upon the assumption that objects closer together in space (e.g. geographical location) will most likely have similar responses. This talk provides an overview of graphical and quantitative methods I developed for the analysis of spatial data to detect geographic trends that could indicate a pandemic or epidemic. Emphasis is on lattice data (also known as areal data or aggregated data) however modeling of geostatistical data and point patterns will be discussed. I will apply these methods to public health data with applications to cancer trends, maternal mortality in the Dominican Republic, and COVID-19 disease surveillance.

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