Title: Modeling the spatiotemporal distributions of ticks and tick-borne diseases.
Abstract: Ticks and tick-borne diseases as well as other arthropods and arthropod-borne diseases are to a large extent regulated by ambient conditions to which these vectors are exposed. This renders the study of their spatial and population dynamics, and to a lesser extent also the prevalence of diseases transmitted by them possible through geospatial and computational methods. Tick-borne diseases have increased in intensity (i.e., number of cases seen) as well as in the spatial distribution over the past couple of decades. The spatiotemporal distribution and exact nature of the driving factors behind such surge are not clearly understood. Fine-resolution vector and disease data at patient-level, and matching remote sensing and demographic/socio-economic data are useful. Additionally, novel geospatial models need to be developed to find meaningful associations and actionable information.
Bio:
Dr. Raghavan is broadly interested in spatially-enabled computational epidemiology of vector-borne and infectious diseases and applications of geospatial approaches for enhancing animal/public health. He extensively uses Geographic Information Science (GIS) and remote-sensing concepts in his research alongside geo-statistical, correlative modeling, and Bayesian modeling approaches for understanding spatio-temporal dynamics of non-stationary epidemiological processes. His current and prior research have identified important spatio-temporal patterns and spatial determinants for vector/water-borne zoonotic diseases from climatic, environmental, and socio-economic themes. Increasingly, his research strives to identify consistencies in complex meteorological variable associations (i.e. climate-change) with vector-borne diseases through the utilization of high-resolution ground-based and NASA Earth Observing System (EOS) datasets.
