PROJECT SUMMARY/ABSTRACT
Mapping spatiotemporal variation in the burden of infectious diseases is critical for targeting interventions like
vaccines and prioritizing at-risk populations, particularly in Low- and Middle-Income Countries (LMICs), where
such diseases are most prevalent. While many priority pathogens have been mapped over entire endemic
regions, enteric infectious diseases (EIDs) have not, due to a perception that the necessary spatially referenced
data on their prevalence and environmental determinants are not available. However, improvements in
differential diagnosis of EIDs, geostatistical methods, and accessibility and accuracy of environmental data mean
that it is now possible to carry out such a mapping. The long-term goal of this proposal is to provide the research
and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific
child health interventions such as novel vaccines. The overall objective is to apply a big data approach to the
modeling of EIDs in combination with advanced geostatistical analyses and global earth observation (EO)-
derived datasets, resulting in generalizable estimates of the geographical distribution of these outcomes and of
their associations with environmental drivers disseminated via an interactive web-based dashboard. The central
hypothesis is that the prevalence of many enteropathogens varies spatiotemporally as a function of climatic,
environmental, and socio-demographic factors in a way that can be modelled using global EO datasets and
similar products. The rationale underlying the proposed research is that it will enable the identification of target
populations for interventions. Specifically, building on existing partnerships between epidemiologists,
climatologists, and hydrologists as well as investigators in numerous LMICs, Dr. Colston will: 1) compile and
maintain a large database of georeferenced results from studies that diagnosed EIDs in children in LMICs; 2)
Apply geostatistical models to EID outcome data (aim 1) and spatiotemporally matched, high resolution
environmental covariates to a). draw inferences about underlying biological processes and b). generate
prediction maps to identify geographical foci of transmission risk. 3) Establish a Planetary Child Health
Observatory (PCHO), an interinstitutional initiative consisting of a). an interactive web-based dashboard and b).
an international consortium of investigators. In addition to these research activities, Dr. Colston proposes a
career development plan that includes mentorship, experiential and peer-to-peer learning, coursework,
publications, and presentations with the objectives of: 1) gaining skills and formal training in geostatistical
inference, biostatistics, and large datasets; 2) expanding expertise in applications of environmental and remote
sensing-derived datasets in health research; 3) transitioning to research independence by securing follow-on
R01 funding. His proposal will be supervised by an outstanding, interdisciplinary mentoring team with
complementary methodological and substantive skills. The project will have a positive impact on public health by
providing data inputs urgently needed for targeting EID interventions to priority populations in LMICs.
