Spatial Video and EpiExplorer: A Field Strategy to Contextualize Enteric Disease Risk in Slum Environments

Abstract

Disease risk associated with contaminated water, poor sanitation, and hygiene in informal settlement environments is conceptually well understood. From an analytical perspective, collecting data at a suitably fine scale spatial and temporal granularity is challenging. Novel mobile methodologies, such as spatial video (SV), can complement more traditional epidemiological field work to address this gap. However, this work then poses additional challenges in terms of analytical visualizations that can be used to both understand sub-neighborhood patterns of risk, and even provide an early warning system. In this paper, we use bespoke spatial programming to create a framework for flexible, fine-scale exploratory investigations of simultaneously-collected water quality and environmental surveys in three different informal settlements of Port-au-Prince, Haiti. We dynamically mine these spatio-temporal epidemiological and environmental data to provide insights not easily achievable using more traditional spatial software, such as Geographic Information System (GIS). The results include sub-neighborhood maps of localized risk that vary monthly. Most interestingly, some of these epidemiological variations might have previously been erroneously explained because of proximate environmental factors and/or meteorological conditions.

The Use of Spatial Video to Map Dynamic and Challenging Environments: A Case Study of Cholera Risk in the Mujoga Relief Camp, D.R.C.

“…In Haiti for example, teams would collect monthly water samples from within the SIS while simultaneously recording the microenvironments around each test site [ 24 ]. This allowed for later mapping and analysis to identify proximate environmental risks, such as the spatial interrelationship between a well and a local drain, or how buckets standing on muddy ground and then lowered into the reservoir could taint the supply, or even how the physical nature of the waterpoint changed over time, such as the concrete degrading and being replaced by an enclosed plinth [ 37 ]. These surveys provided visible (and therefore mappable) context to the epidemiological data being collected.…”

“…Drawing on the team’s data science background, and the way dashboards had developed during the geospatial response to COVID-19, software was developed that allowed for visually interactive ways to explore spatial and numerical associations [ 37 ]. In this way, fluctuations around one water point in terms of the fecal coliform load could be interactively compared against all other water points for that data collection time period, or from the life history of that particular testing site, or as part of a localized pattern of potentially interconnected features [ 37 ]. By adding in the digitized environmental risk scores, and even images of each site, potential causation could also be explored from the revealed patterns.…”