How Remote Sensing of Vibrios Influence Public Health Decisions: Lessons Learned from Bengal Delta and Haiti

Data on disease prevalence and infectious pathogens is sparingly collected/available in region(s) where climatic variability and extreme natural events intersect with population vulnerability (such as lack of access to water and sanitation infrastructure). Therefore, traditional time series modeling approach of calibration and validation of a model is generally inadequate. Hence, prediction of diarrheal infections (such as cholera, Shigella etc) remain a challenge even though disease causing pathogens are strongly associated with modalities of regional climate and weather system. Here we present an algorithm that integrates satellite derived data on several hydroclimatic and ecological processes into a framework that can determine high resolution cholera risk on global scales. Cholera outbreaks can be classified in three forms– epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode endemic (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Using data from multiple satellites (AVHRR, TRMM, GPM, MODIS, VIIRS, GRACE), we will show examples from Haiti, Yemen, Nepal and several other regions where our algorithm has been successful in capturing risk of outbreak of infection in human population. A spatial model validation algorithm will also be presented that has capabilities to self-calibrate as new hydroclimatic and disease data become available. We will also discuss how information on socio-economic status and human behavior influence public health decisions.