Skip to main content

Advertisement

Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Brooker, S., & Michael, E. (2000). The potential of geographical information systems and remote sensing in the epidemiology and control of human helminth infections. Advances in Parasitology, 47, 245–288.

    Article  CAS  Google Scholar 

  • Brooker, S., Hay, S. I., Issae, W., Hall, A., Kihamia, C. M., Lwambo, N. J., et al. (2001). Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data. Tropical Medicine & International Health, 6, 998–1007.

    Article  CAS  Google Scholar 

  • Clements, A. C. A., Lwambo, N. J. S., Blair, L., Nyandindi, U., Kaatano, G., Kinung'hi, S., Webster, J. P., Fenwick, A., & Brooker, S. (2006). Bayesian spatial analysis and disease mapping: Tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania. Tropical Medicine & International Health, 11(4), 490–503.

    Article  Google Scholar 

  • Clements, A. C. A., Reid, H. L., Kelly, G. C., & Hay, S. I. (2013). Further shrinking the malaria map: How can geospatial science help to achieve malaria elimination? Lancet Infectious Diseases, 13(8), 709–718.

    Article  Google Scholar 

  • Dewan, A. M., Corner, R., Hashizume, M., & Ongee, E. T. (2013). Typhoid fever and its association with environmental factors in the Dhaka metropolitan area of Bangladesh: A spatial and time-series approach. PLoS Neglected Tropical Diseases, 7(1), e1998.

    Article  Google Scholar 

  • Doumenge, J. P. (1987). Atlas of the global distribution of schistosomiasis. Bordeaux: Presses Universitaires de Bordeaux.

    Google Scholar 

  • Frenken, K. (2005). Irrigation in Africa in figures: AQUASTAT Survey—2005. FAO Water Reports. Rome: Food and Agriculture Organization of the United Nations.

    Google Scholar 

  • Ghana Statistical Service. (2013). 2010 Population and Housing Census: National Analytical Report. K. Awusabo-Asare (p. 409). Ghana: Ghana Statistical Service.

    Google Scholar 

  • Gryseels, B., Polman, K., Clerinx, J., & Kestens, L. (2006). Human schistosomiasis. Lancet, 368, 1106–1118.

    Article  Google Scholar 

  • Hotez, P. J., Alvarado, M., Basáñez, M. G., Bolliger, I., Bourne, R., Boussinesq, M., Brooker, S. J., Brown, A. S., Buckle, G., Budke, C. M., Carabin, H., Coffeng, L. E., Fèvre, E. M., Fürst, T., Halasa, Y. A., Jasrasaria, R., Johns, N. E., Keiser, J., King, C. H., Lozano, R., Murdoch, M. E., O'Hanlon, S., Pion, S. D. S., Pullan, R. L., Ramaiah, K. D., Roberts, T., Shepard, D. S., Smith, J. L., Stolk, W. A., Undurraga, E. A., Utzinger, J., Wang, M., Murray, C. J. L., & Naghavi, M. (2014). The global burden of disease study 2010: Interpretation and implications for the neglected tropical diseases. PLoS Neglected Tropical Diseases, 8(7), e2865.

    Article  Google Scholar 

  • Jagai, J. S., Sarkar, R., Castronovo, D., Kattula, D., McEntee, J., Ward, H., Kang, G., & Naumova, E. N. (2012). Seasonality of rotavirus in South Asia: A meta-analysis approach assessing associations with temperature, precipitation, and vegetation index. PLoS One, 7(5), e38168.

    Article  CAS  Google Scholar 

  • Kabatereine, N. B., Brooker, S., Tukahebwa, E. M., Kazibwe, F., & Onapa, A. W. (2004). Epidemiology and geography of Schistosoma mansoni in Uganda: Implications for planning control. Tropical Medicine and International Health, 9(3), 372–380.

    Article  Google Scholar 

  • Kabore, A., Biritwum, N. K., Downs, P. W., Soares Magalhães, R. J., Zhang, Y., & Ottesen, E. A. (2013). Predictive vs. empiric assessment of schistosomiasis: Implications for treatment projections in Ghana. PLoS Neglected Tropical Diseases, 7(3), e2051.

    Article  Google Scholar 

  • Kalluri, S., Gilruth, P., Rogers, D., & Szczur, M. (2007). Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: A review. PLoS Pathogens, 3(10), e116–e1371.

    Article  Google Scholar 

  • Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen–Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263 http://www.srh.noaa.gov/jetstream/global/images/Koppen-Geiger.pdf.

    Article  Google Scholar 

  • Kulinkina, A. V. (2017). Community based methods for schistosomiasis prediction and sustainable control in Ghana. PhD Doctoral Thesis, Tufts University.

  • Kulinkina, A. V., Mohan, V. R., Francis, M. R., Kattula, D., Sarkar, R., Plummer, J. D., Ward, H., Kang, G., Balraj, V., & Naumova, E. N. (2016). Seasonality of water quality and diarrheal disease counts in urban and rural settings in South India. Scientific Reports, 6, 20521.

    Article  CAS  Google Scholar 

  • Kulinkina, A. V., Kosinski, K. C., Plummer, J. D., Durant, J. L., Bosompem, K. M., Adjei, M. N., Griffiths, J. K., Gute, D. M., & Naumova, E. N. (2017). Indicators of improved water access in the context of schistosomiasis transmission in rural eastern region, Ghana. Science of the Total Environment, 579, 1745–1755.

    Article  CAS  Google Scholar 

  • Lai, Y. S., Zhou, X. N., Utzinger, J., & Vounatsou, P. (2013). Bayesian geostatistical modeling of soil-transmitted helminth survey data in the People’s Republic of China. Parasites & Vectors, 6, 359.

    Article  Google Scholar 

  • Lai, Y. S., Biedermann, P., Ekpo, U. F., Garba, A., Mathieu, E., Midzi, N., Mwinzi, P., N'Goran, E. K., Raso, G., Assare, R. K., Sacko, M., Schur, N., Talla, I., Tchuente, L. A., Toure, S., Winkler, M. S., Utzinger, J., & Vounatsou, P. (2015). Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: A systematic review and geostatistical analysis. Lancet Infectious Diseases, 15(8), 927–940.

    Article  Google Scholar 

  • Liss, A., Koch, M., & Naumova, E. N. (2014). Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications. Geospatial Health, 8(3), S647–S659.

    Article  Google Scholar 

  • Moomen, A. W., & Dewan, A. (2016). Investigating potential mining induced water stress in Ghana’s north-west gold province. The Extractive Industries and Society, 3(3), 802–812.

    Article  Google Scholar 

  • Moomen, A. W., Dewan, A., & Corner, R. (2016). Landscape assessment for sustainable resettlement of potentially displaced communities in Ghana’s emerging northwest gold province. Journal of Cleaner Production, 133, 701–707.

    Article  Google Scholar 

  • Naumova, E. N., Jagai, J. S., Matyas, B., DeMaria, A., Jr., MacNeill, I. B., & Griffiths, J. K. (2007). Seasonality in six enterically transmitted diseases and ambient temperature. Epidemiology and Infection, 135(2), 281–292.

    Article  CAS  Google Scholar 

  • Scholte, R. G., Gosoniu, L., Malone, J. B., Chammartin, F., Utzinger, J., & Vounatsou, P. (2014). Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models. Acta Tropica, 132, 57–63.

    Article  Google Scholar 

  • Schur, N., Hürlimann, E., Garba, A., Traoré, M. S., Ndir, O., Ratard, R. C., Tchuem Tchuenté, L. A., Kristensen, T. K., Utzinger, J., & Vounatsou, P. (2011). Geostatistical model-based estimates of schistosomiasis prevalence among individuals aged ≤20 years in West Africa. PLoS Neglected Tropical Diseases, 5(6), e1194.

    Article  Google Scholar 

  • Simoonga, C., Utzinger, J., Brooker, S., Vounatsou, P., Appleton, C. C., Stensgaard, A. S., Olsen, A., & Kristensen, T. K. (2009). Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa. Parasitology, 136(13), 1683–1693.

    Article  CAS  Google Scholar 

  • Soares Magalhães, R. J., Biritwum, N. K., Gyapong, J. O., Brooker, S., Zhang, Y., Blair, L., Fenwick, A., & Clements, A. C. (2011). Mapping helminth co-infection and co-intensity: Geostatistical prediction in Ghana. PLoS Neglected Tropical Diseases, 5(6), e1200.

    Article  Google Scholar 

  • Steinmann, P., Keiser, J., Bos, R., Tanner, M., & Utzinger, J. (2006). Schistosomiasis and water resources development: Systematic review, meta-analysis, and estimates of people at risk. Lancet Infectious Diseases, 6(7), 411–425.

    Article  Google Scholar 

  • Walz, Y., Wegmann, M., Dech, S., Raso, G., & Utzinger, J. (2015a). Risk profiling of schistosomiasis using remote sensing: Approaches, challenges and outlook. Parasites and Vectors, 8, 163.

    Article  Google Scholar 

  • Walz, Y., Wegmann, M., Dech, S., Vounatsou, P., Poda, J. N., N'Goran, E. K., Utzinger, J., & Raso, G. (2015b). Modeling and validation of environmental suitability for schistosomiasis transmission using remote sensing. PLoS Neglected Tropical Diseases, 9(11), e0004217.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elena N. Naumova.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Geospatial Technology in Environmental Health Applications

Electronic supplementary material

ESM 1

(DOCX 1817 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 (Suppl 2), 301 (2019). https://doi.org/10.1007/s10661-019-7411-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-7411-6

Keywords

Navigation