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Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases

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  • Nicholas A S Hamm
  • Ricardo J Soares Magalhães
  • Archie C A Clements

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.

Suggested Citation

  • Nicholas A S Hamm & Ricardo J Soares Magalhães & Archie C A Clements, 2015. "Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(12), pages 1-24, December.
    • Handle: RePEc:plo:pntd00:0004164
    DOI: 10.1371/journal.pntd.0004164
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    References listed on IDEAS

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    1. Xian-Hong Wang & Xiao-Nong Zhou & Penelope Vounatsou & Zhao Chen & Jürg Utzinger & Kun Yang & Peter Steinmann & Xiao-Hua Wu, 2008. "Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 2(6), pages 1-9, June.
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    3. Jörn P W Scharlemann & David Benz & Simon I Hay & Bethan V Purse & Andrew J Tatem & G R William Wint & David J Rogers, 2008. "Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-13, January.
    4. David J. Rogers & Sarah E. Randolph & Robert W. Snow & Simon I. Hay, 2002. "Satellite imagery in the study and forecast of malaria," Nature, Nature, vol. 415(6872), pages 710-715, February.
    5. Achille Kabore & Nana-Kwadwo Biritwum & Philip W Downs & Ricardo J Soares Magalhaes & Yaobi Zhang & Eric A Ottesen, 2013. "Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 7(3), pages 1-8, March.
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    1. Zhichao Li & Helen Gurgel & Nadine Dessay & Luojia Hu & Lei Xu & Peng Gong, 2020. "Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation," IJERPH, MDPI, vol. 17(12), pages 1-29, June.
    2. Andrea L. Araujo Navas & Frank Osei & Lydia R. Leonardo & Ricardo J. Soares Magalhães & Alfred Stein, 2019. "Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Drivers of Infection," IJERPH, MDPI, vol. 16(2), pages 1-17, January.
    3. Fuqiang Dai & Hao Liu & Xia Zhang & Qing Li, 2021. "Exploring the Emerging Trends of Spatial Epidemiology: A Scientometric Analysis Based on CiteSpace," SAGE Open, , vol. 11(4), pages 21582440211, November.

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