Abstract: Vector-borne and zoonotic pathogens comprise a substantial portion of the global disease burden causing ∼1.4 million deaths annually, and account for approximately 17% of the entire disease burden caused by parasitic and infectious diseases. Current Public Health and Department of Defense surveillance systems track individual infectious disease cases to report disease trends across populations, but these are retrospective and do not provide predictive information that could identify high-risk areas to better public health as well as deploying military personnel. Epidemiologies of many vector-borne pathogens are driven by climate and environmental conditions that critically influence vector survival, reproduction, biting rates, feeding patterns, pathogen incubation and replication, and the efficiency of pathogen transmission among multiple hosts. Specific patterns of climate and weather anomalies preceding increased vector populations and resulting disease outbreaks are extensively documented for dengue, chikungunya, Rift Valley fever, and plague. To address this gap in early warning and response to vectorborne disease threats, we are designing a unified application that utilizes multi-decade satellite derived measurements of climate variables map and forecast the risk of various diseases including chikungunya, dengue, Rift Valley fever and hantavirus. To design such an integrated system, we are (1) compiling historical outbreak data from various sources including ProMED and PAHO, (2) global climate data from NOAA and NASA including rainfall, land surface temperature, (3) Population data from the Socioeconomic Data and Applications Center (SEDAC) and LandScan Population database from ORNL, (4) Various mosquito vector distributions from VectorMap (Walter Reed Biosystematics Unit) and VectorBase (National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Center (BRC). We plan to utilize various machine learning methods to combine these complex data sets to map and forecast on a monthly to seasonal basis the risk of various disease threats globally.
