Surveillance data from large and small spatial scales play an essential role in public health and scientific research, but these data are subject to missing observations, delays in reporting, and observation biases. The IMPETUS study aims to develop novel methods for dealing with bias, missing data and uncertainty in regional, local and point pattern statistics. Dr. Cummings, in collaboration with researchers at Johns Hopkins and UMass Amherst, is working to create statistical and modeling methodologies to correct for biases in surveillance data, impute missing data, predict the course of epidemics, and appropriately characterize the uncertainty in estimates and predictions at relevant spatial scales. Methods will be tested and validated using surveillance data of dengue transmission in Thailand, but should be applicable to a wide variety of diseases and contexts.