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Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania
Open accessAbstract
Summary: Objective To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on‐going national control programme in Tanzania. Methods Bayesian geostatistical models were developed using parasitological data from 143 schools. Results In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non‐significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. Conclusion Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.
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Cited by
Schistosomiasis: challenges for control, treatment and drug resistance
Fenwick, A., & Webster, J. P. (2006) · Current Opinion in Infectious Diseases · doi:10.1097/01.qco.0000247591.13671.6a · Cited by 254
Self-citationThe risks of malaria infection in Kenya in 2009
Noor, A. M., Gething, P. W., Alegana, V. A., Patil, A. P., Hay, S. I., Muchiri, E., Juma, E., & Snow, R. W. (2009) · BMC Infectious Diseases · doi:10.1186/1471-2334-9-180 · Cited by 120
Bayesian modelling of geostatistical malaria risk data
Gosoniu, L., Vounatsou, P., Sogoba, N., & Smith, T. (2006) · Geospatial health · doi:10.4081/gh.2006.287 · Cited by 111
Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
Diggle, P. J., & Giorgi, E. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1123158 · Cited by 56
Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases
Hamm, N. A. S., Soares Magalhães, R. J., & Clements, A. C. A. (2015) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0004164 · Cited by 48
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Metadata
- Authors
- Clements, A. C. A., Lwambo, N. J. S., Blair, L., Nyandindi, U., Kaatano, G., Kinung'hi, S., Webster, J. P., Fenwick, A., & Brooker, S.
- Venue
- Tropical Medicine & International Health
- Publisher
- Wiley
- Published
- 2006-03-23
- Volume
- 11
- Issue
- 4
- Pages
- 490-503
- ISSN
- 1360-2276 (ISSN-L)
- 1365-3156
- OpenAlex
- W1530599454
- Cited by
- 218
- References
- 0
- FWCI
- 8.54
- Type
- journal article
- Language
- en
- Open access
- green
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- 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. https://doi.org/10.1111/j.1365-3156.2006.01594.x
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