The Planetary Child Health Observatory - from Earth Observation-informed predictive mapping to early warning systems for enteric infectious diseases in low- and middle-income countries.
Abstract
Diarrhea of infectious etiology remains a leading cause of childhood illness and death and carries lifelong health and economic impacts. Dozens of bacterial, viral, and parasitic organism species transmitted via contaminated water, aerosols, food, or surfaces can cause diarrheal disease and each has distinct human-environmental interactions. Climatic variability and extreme weather events appear to play a larger role in enteric infectious disease (EID) transmission than had previously been recognized, and remote sensing and hydrometeorological modeling innovations now make it possible estimate spatiotemporal variation in these factors over large geographic areas. The Planetary Child Health Observatory is an interinstitutional initiative that arose out of partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries (LMICs) that aims to produce, curate, and disseminate spatial data products relating to the distribution of EIDs and their environmental and sociodemographic determinants. The initiative is compiling a growing database of georeferenced PCR results from stool samples - currently numbering more than 80,000 - from studies in 26 LMICs. These are matched spatiotemporally with climate and environmental variables extracted from global earth observation (EO)-derived datasets and other sources and fitted within geostatistical models to predict the geographical distribution of pathogen-specific attributable disease risk. Early analyses have found strong associations of soil moisture, humidity, and temperature with almost all EID species, likely due to how they impact the survival of microbes outside of the host, while surface runoff and vegetation indices are predictive of risk only for specific organisms. As the initiative moves into its next phase, the approach will be systematized and incorporated into early warning systems to predict surges in EID cases and results will be disseminated through an interactive web-based dashboard.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGH22A..03C