The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Environmental Monitoring and Assessment volume 191, Article number: 301 (2019) Cite this article

Abstract

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008–2015) of monthly schistosomiasis cases reported into Ghana’s national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3–1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.

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Acknowledgements

We acknowledge the support of Tufts Innovates project “Stats beyond the Basics” for providing the discussion platform for graduate students and faculty participating in the preparation of this manuscript (MW, AK, MC, KK, and EN). We thank the reviewers for their thoughtful suggestions and Dr. Fazlay Faruque for encouraging us to prepare this article for a special issue of Environmental Monitoring and Assessment.

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Author notes

  1. Madeline Wrable and Alexandra V. Kulinkina contributed equally to this work.

Affiliations

  1. School of Engineering, Tufts University, Medford, MA, USA

    Madeline Wrable, Alexandra V. Kulinkina, Alexander Liss, David M. Gute & Elena N. Naumova

  2. Center for Remote Sensing, Boston University, Boston, MA, USA

    Magaly Koch

  3. Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA, 02111, USA

    Melissa S. Cruz & Elena N. Naumova

  4. Ghana Health Service, Neglected Tropical Diseases Program, Accra, Ghana

    Nana-Kwadwo Biritwum

  5. Ghana Health Service, Policy, Planning, Monitoring, and Evaluation Division, Accra, Ghana

    Anthony Ofosu

  6. School of Arts and Sciences, Tufts University, Medford, MA, USA

    Karen C. Kosinski

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  1. Madeline Wrable
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Wrable, M., Kulinkina, A.V., Liss, A. et al. The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana. Environ Monit Assess 191, 301 (2019). https://doi.org/10.1007/s10661-019-7411-6

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Keywords

  • Remote sensing
  • Climate classification
  • Schistosomiasis
  • Surveillance