Software to facilitate remote sensing data access for disease early warning systems

Abstract

Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

“…Learning parasite transmission models that take a fuller account of heterogeneous dynamics across a spatial domain is a difficult task, but the increasing availability of geolocated demographic, intervention, and disease data [ 23 – 26 ] together with growing advances made in computational science approaches to knowledge discovery, particularly in the areas of (1) high performance grid-based computing and programming [ 8 , 11 ], (2) data discovery, integration, and assembly [ 11 , 19 , 27 – 31 ], and (3) data-driven approaches for inferring models from measurements [ 32 – 38 ], mean that simulating disease dynamics and responses to interventions effectively across heterogeneous spatially structured environments at large scales are now becoming increasingly feasible. Bayesian data-driven modeling frameworks have received considerable attention in this regard given their ability for not only facilitating the induction of a dynamical system from data, but also in the use of multiple data sources for constraining the parameters of a model to capture the local transmission features of a spatial setting [ 21 , 22 , 33 , 39 – 41 ].…”

Continental-Scale, Data-Driven Predictive Assessment of Eliminating the Vector-Borne Disease, Lymphatic Filariasis, in Sub-Saharan Africa by 2020

“…While we have created a plausible data management and scientific workflow system to tackle the issues of discovery, assembly, and data transformations/interpolations required to provide the input data for identifying the locally applicable LF models, we note that there is a need to automate our current approaches to speed up these data delivery and processing activities. We are currently working with computer scientists to develop a server-side infection data processing system based on using data warehouse principles and methods [27,30,31] to address this issue. A similar requirement for running dataintensive models across a large heterogeneous spatial domain is looking at advances in software and hardware to speed up the computational discovery and simulation process.…”

“…Learning parasite transmission models that take a fuller account of heterogeneous dynamics across a spatial domain is a difficult task, but the increasing availability of geolocated demographic, intervention, and disease data [23][24][25][26] together with growing advances made in computational science approaches to knowledge discovery, particularly in the areas of (1) high performance grid-based computing and programming [8,11], (2) data discovery, integration, and assembly [11,19,[27][28][29][30][31], and (3) datadriven approaches for inferring models from measurements [32][33][34][35][36][37][38], mean that simulating disease dynamics and responses to interventions effectively across heterogeneous spatially structured environments at large scales are now becoming increasingly feasible. Bayesian data-driven modeling frameworks have received considerable attention in this regard given their ability for not only facilitating the induction of a dynamical system from data, but also in the use of multiple data sources for constraining the parameters of a model to capture the local transmission features of a spatial setting [21,22,33,[39][40][41].…”

Thematic issue on modelling human and ecological health risks

“…Software and models that can provide vital data to predict the location and severity of health risks can support planning of public health interventions. The paper by Liu et al (2015) introduces the EASTWeb 2 software, which supports early warning systems for mosquitoborne diseases. EASTWeb has been integrated with the R environment to carry out modeling and mapping and has been extensively tested through applications to support mosquitoborne disease forecasting for West Nile virus in 2 Epidemiological Applications of Spatial Technologies the United States and epidemic malaria in the highlands of Ethiopia.…”