Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases

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.

[1]  Dylan B. George,et al.  Big Data Opportunities for Global Infectious Disease Surveillance , 2013, PLoS medicine.

[2]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[3]  Peter M. Atkinson,et al.  Spatial variation in land cover and choice of spatial resolution for remote sensing , 2004 .

[4]  C. Donlon,et al.  The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission , 2012 .

[5]  S. Hay An overview of remote sensing and geodesy for epidemiology and public health application. , 2000, Advances in parasitology.

[6]  A. Tatem,et al.  Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery. , 2004, Remote sensing of environment.

[7]  Roy M. Anderson,et al.  Population dynamics of human helminth infections: control by chemotherapy , 1982, Nature.

[8]  Sylvie Servigne,et al.  Quality Components, Standards, and Metadata , 2010 .

[9]  U. Kitron,et al.  Upscale or downscale: applications of fine scale remotely sensed data to Chagas disease in Argentina and schistosomiasis in Kenya. , 2006, Geospatial health.

[10]  R A Holmes,et al.  Temperature data from satellite imagery and the distribution of schistosomiasis in Egypt. , 1994, The American journal of tropical medicine and hygiene.

[11]  S. M. Jong,et al.  Optimizing spatial image support for quantitative mapping of natural vegetation , 2009 .

[12]  T. Groen,et al.  Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling , 2011 .

[13]  Jonathan A. Patz,et al.  Land Use Change and Human Health , 2011, Encyclopedia of Environmental Health.

[14]  Christoph Stasch,et al.  Spatio-temporal aggregation of European air quality observations in the Sensor Web , 2012, Comput. Geosci..

[15]  S. Pocock,et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. , 2007, Preventive medicine.

[16]  Timothy A. Warner,et al.  The SAGE Handbook of Remote Sensing , 2009 .

[17]  Penelope Vounatsou,et al.  Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard , 2008, PLoS neglected tropical diseases.

[18]  M. Disney,et al.  Terrestrial ecosystems from space: a review of earth observation products for macroecology applications , 2012 .

[19]  Gail M Williams,et al.  Impact of anthropogenic and natural environmental changes on Echinococcus transmission in Ningxia Hui Autonomous Region, the People’s Republic of China , 2012, Parasites & Vectors.

[20]  Y. LindaJ. Combining Incompatible Spatial Data , 2003 .

[21]  A. Tatem,et al.  Terra and Aqua: new data for epidemiology and public health. , 2004, International journal of applied earth observation and geoinformation : ITC journal.

[22]  F. M. Danson,et al.  ECHINOCOCCUS MULTILOCULARIS: THE ROLE OF SATELLITE REMOTE SENSING, GIS AND SPATIAL MODELLING , 2004 .

[23]  Qian Wang,et al.  Landscape Composition and Spatial Prediction of Alveolar Echinococcosis in Southern Ningxia, China , 2008, PLoS neglected tropical diseases.

[24]  International Journal of Applied Earth Observation and Geoinformation , 2017 .

[25]  N. Cressie,et al.  Spatial Statistics in the Presence of Location Error with an Application to Remote Sensing of the Environment , 2003 .

[26]  Romy R. Ravines,et al.  Impact of Environment and Social Gradient on Leptospira Infection in Urban Slums , 2008, PLoS neglected tropical diseases.

[27]  Peng Gong,et al.  Global land cover mapping using Earth observation satellite data: Recent progresses and challenges , 2015 .

[28]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[29]  R. Bergquist,et al.  Identification of Parasite-Host Habitats in Anxiang County, Hunan Province, China Based on Multi-Temporal China-Brazil Earth Resources Satellite (CBERS) Images , 2013, PloS one.

[30]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[31]  Fabio Remondino,et al.  UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING - CURRENT STATUS AND FUTURE PERSPECTIVES - , 2012 .

[32]  Simon J Brooker,et al.  The global limits and population at risk of soil-transmitted helminth infections in 2010 , 2012, Parasites & Vectors.

[33]  Penelope Vounatsou,et al.  Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People’s Republic of China , 2013, Parasites & Vectors.

[34]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[35]  Paul M. Emerson,et al.  Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan , 2010, PLoS neglected tropical diseases.

[36]  P Vounatsou,et al.  Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa , 2009, Parasitology.

[37]  Peter M. Atkinson,et al.  On the effect of positional uncertainty in field measurements on the atmospheric correction of remotely sensed imagery , 2004 .

[38]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[39]  Richard A. Wadsworth,et al.  What's in a Name? Semantics, Standards and Data Quality , 2009 .

[40]  Christoph Stasch,et al.  New Generation Sensor Web Enablement , 2011, Sensors.

[41]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[42]  S. Hay,et al.  Satellite imagery in the study and forecast of malaria , 2002, Nature.

[43]  Russell G. Congalton,et al.  How to Assess the Accuracy of Maps Generated from Remotely Sensed Data , 2010 .

[44]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[45]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[46]  S. Hay,et al.  Deriving meteorological variables across Africa for the study and control of vector‐borne disease: a comparison of remote sensing and spatial interpolation of climate , 1999, Tropical medicine & international health : TM & IH.

[47]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[48]  Archie C A Clements,et al.  The applications of model-based geostatistics in helminth epidemiology and control. , 2011, Advances in parasitology.

[49]  Dan Cornford,et al.  Managing uncertainty in integrated environmental modelling: The UncertWeb framework , 2013, Environ. Model. Softw..

[50]  Kate Beard,et al.  Communication and Use of Spatial Data Quality Information in GIS , 2010 .

[51]  S. Brooker,et al.  Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania , 2006, Tropical medicine & international health : TM & IH.

[52]  Russ Burtner,et al.  INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS REVIEW Open Access , 2022 .

[53]  Penelope Vounatsou,et al.  Bayesian risk maps for Schistosoma mansoni and hookworm mono-infections in a setting where both parasites co-exist. , 2007, Geospatial health.

[54]  P M Atkinson,et al.  Linking remote sensing, land cover and disease. , 2000, Advances in parasitology.

[55]  Russell G. Congalton,et al.  Global Land Cover Mapping: A Review and Uncertainty Analysis , 2014, Remote. Sens..

[56]  S. Hay,et al.  Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. , 1996, Annals of tropical medicine and parasitology.

[57]  S. Brooker,et al.  Global epidemiology, ecology and control of soil-transmitted helminth infections. , 2006, Advances in parasitology.

[58]  David L. Smith,et al.  A new world malaria map: Plasmodium falciparum endemicity in 2010 , 2011, Malaria Journal.

[59]  Steven M. De Jong,et al.  Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random Forests , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[60]  A. Dobson,et al.  Leptospirosis in American Samoa – Estimating and Mapping Risk Using Environmental Data , 2012, PLoS neglected tropical diseases.

[61]  R Moyeed,et al.  Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa , 2006, Parasitology.

[62]  K. Gage,et al.  Potential Influence of Climate Change on Vector-Borne and Zoonotic Diseases: A Review and Proposed Research Plan , 2010, Environmental health perspectives.

[63]  Valérie Obsomer,et al.  Mapping the distribution of Loa loa in Cameroon in support of the African Programme for Onchocerciasis Control , 2004, Filaria journal.

[64]  A. Tatem,et al.  Global environmental data for mapping infectious disease distribution. , 2006, Advances in parasitology.

[65]  F M Danson,et al.  Multi-scale spatial analysis of human alveolar echinococcosis risk in China , 2003, Parasitology.

[66]  Elisabeth A. Addink,et al.  The use of high-resolution remote sensing for plague surveillance in Kazakhstan , 2010 .

[67]  Edzer Pebesma,et al.  Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution , 2014 .

[68]  D. Lo Seen,et al.  The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal) , 2010 .

[69]  Peter M. Atkinson,et al.  A per-pixel, non-stationary mixed model for empirical line atmospheric correction in remote sensing , 2012 .

[70]  Michael F. Goodchild,et al.  Spatial Data Quality , 2002 .

[71]  David L. Smith,et al.  A World Malaria Map: Plasmodium falciparum Endemicity in 2007 , 2009, PLoS medicine.

[72]  G. Foody,et al.  Remote Sensing Scale and Data Selection Issues , 2009 .

[73]  D. Roy,et al.  The MODIS Land product quality assessment approach , 2002 .

[74]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[75]  Jennifer L. Dungan,et al.  A balanced view of scale in spatial statistical analysis , 2002 .

[76]  Peter M. Atkinson,et al.  Bayesian Geostatistical Analysis and Prediction of Rhodesian Human African Trypanosomiasis , 2010, PLoS neglected tropical diseases.

[77]  A. Tatem,et al.  Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data , 2008, PloS one.

[78]  Robert Jeansoulin,et al.  Spatial Data Quality: Concepts , 2010 .

[79]  A J Graham,et al.  Issues of scale and uncertainty in the global remote sensing of disease. , 2006, Advances in parasitology.

[80]  X. Yang,et al.  An integrated view of data quality in Earth observation , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[81]  Vincent Herbreteau,et al.  Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. , 2007, Health & place.

[82]  Danny Lo Seen,et al.  Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[83]  S. Hay,et al.  Using NOAA-AVHRR data to model human helminth distributions in planning disease control in Cameroon, West Africa , 2002 .

[84]  S. de Bruin,et al.  Linear trends in seasonal vegetation time series and the modifiable temporal unit problem , 2012 .

[85]  D. Cole,et al.  A systematic review of the application and utility of geographical information systems for exploring disease-disease relationships in paediatric global health research: the case of anaemia and malaria , 2013, International Journal of Health Geographics.

[86]  Penelope Vounatsou,et al.  Determining Treatment Needs at Different Spatial Scales Using Geostatistical Model-Based Risk Estimates of Schistosomiasis , 2012, PLoS neglected tropical diseases.

[87]  Penelope Vounatsou,et al.  Geostatistical Model-Based Estimates of Schistosomiasis Prevalence among Individuals Aged ≤20 Years in West Africa , 2011, PLoS neglected tropical diseases.

[88]  Qinghua Ye,et al.  Handling uncertainties in image mining for remote sensing studies , 2009 .

[89]  L. R. Beck,et al.  Perspectives Perspectives Perspectives Perspectives Perspectives Remote Sensing and Human Health: New Sensors and New Opportunities , 2022 .

[90]  Thierry Toutin,et al.  Fine Spatial Resolution Optical Sensors , 2009 .

[91]  Eric A. Ottesen,et al.  Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana , 2013, PLoS neglected tropical diseases.

[92]  David L. Glackin Observational Systems, Satellite , 2014, Encyclopedia of Remote Sensing.

[93]  A. K. Bregt,et al.  Spatial Data Quality , 2008, Encyclopedia of GIS.

[94]  Stefan Dech,et al.  Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook , 2015, Parasites & Vectors.

[95]  Penelope Vounatsou,et al.  RANDOM SPATIAL DISTRIBUTION OF SCHISTOSOMA MANSONI AND HOOKWORM INFECTIONS AMONG SCHOOL CHILDREN WITHIN A SINGLE VILLAGE , 2003, The Journal of parasitology.

[96]  Pedro J. Leitão,et al.  Effects of species and habitat positional errors on the performance and interpretation of species distribution models , 2009 .

[97]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[98]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[99]  Christovam Barcellos,et al.  Geographical Scale Effects on the Analysis of Leptospirosis Determinants , 2014, International journal of environmental research and public health.

[100]  M. Tanner,et al.  An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[101]  S. Running,et al.  Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data , 2008 .

[102]  Andrew K. Skidmore,et al.  Where is positional uncertainty a problem for species distribution modelling , 2014 .

[103]  Gerard C Kelly,et al.  Further shrinking the malaria map: how can geospatial science help to achieve malaria elimination? , 2013, The Lancet. Infectious diseases.

[104]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[105]  J P Gonzalez,et al.  Sizing up human health through remote sensing: uses and misuses. , 2005, Parassitologia.

[106]  Marcel Tanner,et al.  Integrated urban malaria control: a case study in dar es salaam, Tanzania. , 2004, The American journal of tropical medicine and hygiene.

[107]  Penelope Vounatsou,et al.  Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia , 2013, Parasites & Vectors.

[108]  Ricardo J Soares Magalhães,et al.  Role of malnutrition and parasite infections in the spatial variation in children's anaemia risk in northern Angola. , 2013, Geospatial health.

[109]  Penelope Vounatsou,et al.  Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling. , 2013, Acta tropica.

[110]  Peter M. Atkinson,et al.  Downscaling in remote sensing , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[111]  Mark Gahegan,et al.  Using Metadata to Link Uncertainty and Data Quality Assessments , 2006 .

[112]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[113]  Marc F. P. Bierkens,et al.  Upscaling and downscaling methods for environmental research , 2000 .

[114]  Erika Upegui,et al.  GeoEye Imagery and Lidar Technology for Small-area Population Estimation: An Epidemiological Viewpoint , 2012 .

[115]  J B Malone,et al.  Use of satellite remote sensing and geographic information systems to model the distribution and abundance of snail intermediate hosts in Africa: a preliminary model for Biomphalaria pfeifferi in Ethiopia. , 2001, Acta tropica.

[116]  S. Pocock,et al.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration , 2007, PLoS medicine.

[117]  Nicholas A. S. Hamm,et al.  Spatio-Temporal Assessment of Tuz Gölü, Turkey as a Potential Radiometric Vicarious Calibration Site , 2014, Remote. Sens..

[118]  David L. Smith,et al.  Global mapping of infectious disease , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[119]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[120]  Ricardo J Soares Magalhães,et al.  Spatial heterogeneity of haemoglobin concentration in preschool-age children in sub-Saharan Africa. , 2011, Bulletin of the World Health Organization.

[121]  Alan Fenwick,et al.  A Comparative Study of the Spatial Distribution of Schistosomiasis in Mali in 1984–1989 and 2004–2006 , 2009, PLoS neglected tropical diseases.

[122]  Alan Fenwick,et al.  Mapping Helminth Co-Infection and Co-Intensity: Geostatistical Prediction in Ghana , 2011, PLoS neglected tropical diseases.

[123]  Ricardo J. Soares Magalhães,et al.  Mapping the Risk of Anaemia in Preschool-Age Children: The Contribution of Malnutrition, Malaria, and Helminth Infections in West Africa , 2011, PLoS medicine.

[124]  Alfred Stein,et al.  Variance-based sensitivity analysis of BIOME-BGC for gross and net primary production , 2014 .

[125]  Alan Fenwick,et al.  Mapping the Probability of Schistosomiasis and Associated Uncertainty, West Africa , 2008, Emerging infectious diseases.

[126]  F. Mark Danson,et al.  Landscape Dynamics and Risk Modeling of Human Alveolar Echinococcosis , 2004 .

[127]  Pebesma Edzer,et al.  Managing uncertainty in integrated environmental modelling frameworks: The UncertWeb framework , 2012 .

[128]  Nicholas A. S. Hamm,et al.  Analysing the effect of different aggregation approaches on remotely sensed data , 2013 .

[129]  Simon I. Hay,et al.  The impact of remote sensing on the study and control of invertebrate intermediate hosts and vectors for disease , 1997 .

[130]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[131]  M. Szczur,et al.  Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review , 2007, PLoS pathogens.

[132]  C. Justice,et al.  A framework for the validation of MODIS Land products , 2002 .

[133]  Clement Atzberger,et al.  Why confining to vegetation indices? Exploiting the potential of improved spectral observations using radiative transfer models , 2011, Remote Sensing.

[134]  Ian Riley,et al.  Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu , 2010, Malaria Journal.

[135]  David P. Roy,et al.  MODIS Land Data Products: Generation, Quality Assurance and Validation , 2010 .

[136]  Jo-An Atkinson,et al.  Environmental changes impacting Echinococcus transmission: research to support predictive surveillance and control , 2013, Global change biology.

[137]  Richard A. Wadsworth,et al.  What is Land Cover? , 2005 .

[138]  S I Hay,et al.  Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease. , 1996, Annals of tropical medicine and parasitology.

[139]  Gary J. Hunter,et al.  WHAT COMMUNICATES QUALITY TO THE SPATIAL DATA CONSUMER , 2007 .

[140]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..