Research Article: Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases

Date Published: December 17, 2015

Publisher: Public Library of Science

Author(s): Nicholas A. S. Hamm, Ricardo J. Soares Magalhães, Archie C. A. Clements, Shan Lv.

Abstract: Earth observation (EO) is the use of remote sensing and in situ observations to gather data on the environment. It finds increasing application in the study of environmentally modulated neglected tropical diseases (NTDs). Obtaining and assuring the quality of the relevant spatially and temporally indexed EO data remain challenges. Our objective was to review the Earth observation products currently used in studies of NTD epidemiology and to discuss fundamental issues relating to spatial data quality (SDQ), which limit the utilization of EO and pose challenges for its more effective use. We searched Web of Science and PubMed for studies related to EO and echinococossis, leptospirosis, schistosomiasis, and soil-transmitted helminth infections. Relevant literature was also identified from the bibliographies of those papers. We found that extensive use is made of EO products in the study of NTD epidemiology; however, the quality of these products is usually given little explicit attention. We review key issues in SDQ concerning spatial and temporal scale, uncertainty, and the documentation and use of quality information. We give examples of how these issues may interact with uncertainty in NTD data to affect the output of an epidemiological analysis. We conclude that researchers should give careful attention to SDQ when designing NTD spatial-epidemiological studies. This should be used to inform uncertainty analysis in the epidemiological study. SDQ should be documented and made available to other researchers.

Partial Text: Earth observation (EO) of the environment has found increasing application in epidemiology and public health over the past 40 years [1–3]. It has been used mainly to provide data on the biological and physical environmental variables that determine the distribution of infectious disease, either directly or through their influence on the host, vector, or pathogen habitat. The use of EO in the study of neglected tropical diseases (NTD) is receiving increased attention [3–8].

The term Earth observation (EO) has commonly been used interchangeably with remote sensing (RS); however, current use of the term is broader and includes in situ observations of the environment [36,37]. EO products may be compiled from RS, in situ data, or some combination of the two. In their conceptualization of Observations and Measurements, the Open Geospatial Consortium (OGC) takes a broader view. They define an observation as “an act associated with a discrete time instant or period through which a number, term or other symbol is assigned to a phenomenon. It involves application of a specified procedure, such as a sensor, instrument, algorithm or process chain” [38]. As such, an observation could be a direct measurement (e.g., thermometer reading), a remotely sensed measurement, or the output of a process chain. The process chain could be routine processing from digital numbers to give a product such as the normalized difference vegetation index (NDVI) or the output from a complex environmental process-based simulator (e.g., weather prediction). This conceptualization is useful because it provides a common platform for conceptualizing data produced using different processes. Note that a disease map, a common output of a spatial epidemiological investigation, is itself an observation (although not an EO). Disease maps can, and have, been used as an input to a subsequent analysis [26]. In this paper, we adopt the above broad interpretation of EO as providing data that relate to the environment. We focus mainly on RS and products derived from RS data, although datasets derived from in situ observations are also considered.

We distinguish between static and dynamic environmental variables [3,18]. Static variables include land use and land cover (LULC) and digital elevation models (DEM). Dynamic variables include land surface and vegetation seasonal dynamics as well as seasonal meteorological dynamics. Below, we review EO products that provide these variables.

Several recent studies of NTD epidemiology have applied Bayesian spatial prediction and emphasize the importance of quantifying uncertainty in the predictions that make up the map [26,70,76]. This prediction uncertainty is based on the Bayesian model and is quantified by, for example, the variance or the width of the credible interval. Prediction uncertainty is location-specific and has implications for the interpretation of the results, for deciding the locations of future surveys, and for intervention planning [20,76,97].

EO has found increasing application in public health over the past 40 years and, more recently, in the spatial epidemiology of NTDs. During that time, the research questions have become more complex, and there is an increasing and urgent need to make more informed decisions about the use of suitable EO data in the context of a wider range of health and geospatial tools. At the same time, the volume and diversity of EO data has increased and will continue to increase. In order to make effective use of the data, it is necessary to be critical about what is required and what the relevant spatial and temporal scales are, and to quantify the uncertainty in the EO data as well as the geographically referenced socioeconomic and health data. SDQ should be documented by researchers and made public so that it can be queried to identify suitable datasets, and propagated through epidemiological analyses so that uncertainty in predictions can be evaluated fully. This will require the further development of analytical methods that are appropriate for spatial-temporal data as well as user-friendly software tools. Furthermore, it is necessary to harness recent developments in image analysis and the analysis of time-series data in order to extract useful information from EO data and to model the impact of environmental change on NTDs. Finally, it is necessary to properly evaluate competing modelling approaches and EO data products for both research studies and operational applications.



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