Waterborne Disease Outbreaks in the Post-Disaster Scenario: Can High-Resolution Earth Observations and Smallsat Imagery Enhance Cholera Forecasting?

Recent studies have focused on large-scale surveillance approaches - synthesizing remotely sensed environmental variables and hydroclimatic drivers- to develop early warning systems for waterborne diseases. While these studies have been useful for understanding disease progression in endemic regions, their applicability in epidemic and post-disaster settings have been limited due to paucity in observations and the lack of high-resolution imagery of affected areas, vulnerable populations, and infrastructure damage, etc. The increasing data availability at high temporal and spatial resolutions combined with advanced data analytics approaches provide the opportunity to fill in these data gaps for a better understanding of the determinants and drivers of epidemic waterborne disease outbreaks. The high revisit rates of smallsat datasets also enable temporal machine-learning paradigms that significantly improve the accuracy and robustness of existing models for estimating infrastructure damage, population displacement, and flood extents from aerial and satellite imagery, forming highly descriptive inputs to disease prediction models.

Using cholera as a signature disease, this study focuses on outbreaks in Haiti, Mozambique, and Bangladesh to combine smallsat imagery (such as Maxar WorldView, QuickBird, and GeoEye) with existing mission datasets (such as GPM, MODIS, SMAP, SRTM, Jason-3) to develop a comprehensive post-disaster disease prediction model for analyzing the before and after scenario for any natural disaster (hurricanes, coastal storms, floods, and other extreme events). This analysis will significantly increase our understanding of the threats to WASH access, damage to road networks, population displacement, infrastructure, and essential services following natural disasters that may lead to waterborne disease threats, and help improve policy interventions and community engagement approaches to better manage environmental health impacts.