Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

Research output: Contribution to journalJournal articleResearchpeer-review

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Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. / Ekpo, Uwem F.; Hürlimann, Eveline; Schur, Nadine; Oluwole, Akinola S.; Abe, Eniola M.; Mafe, Margaret A.; Nebe, Obiageli J.; Isiyaku, Sunday; Olamiju, Francisca; Kadiri, Mukaila; Poopola, Temitope O. S.; Braide, Eka I.; Saka, Yisa; Mafiana, Chiedu F.; Kristensen, Thomas K.; Utzinger, Jürg; Vounatsou, Penelope.

In: Geospatial Health, Vol. 7, No. 2, 2013, p. 355-366.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Ekpo, UF, Hürlimann, E, Schur, N, Oluwole, AS, Abe, EM, Mafe, MA, Nebe, OJ, Isiyaku, S, Olamiju, F, Kadiri, M, Poopola, TOS, Braide, EI, Saka, Y, Mafiana, CF, Kristensen, TK, Utzinger, J & Vounatsou, P 2013, 'Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling', Geospatial Health, vol. 7, no. 2, pp. 355-366.

APA

Ekpo, U. F., Hürlimann, E., Schur, N., Oluwole, A. S., Abe, E. M., Mafe, M. A., ... Vounatsou, P. (2013). Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospatial Health, 7(2), 355-366.

Vancouver

Ekpo UF, Hürlimann E, Schur N, Oluwole AS, Abe EM, Mafe MA et al. Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospatial Health. 2013;7(2):355-366.

Author

Ekpo, Uwem F. ; Hürlimann, Eveline ; Schur, Nadine ; Oluwole, Akinola S. ; Abe, Eniola M. ; Mafe, Margaret A. ; Nebe, Obiageli J. ; Isiyaku, Sunday ; Olamiju, Francisca ; Kadiri, Mukaila ; Poopola, Temitope O. S. ; Braide, Eka I. ; Saka, Yisa ; Mafiana, Chiedu F. ; Kristensen, Thomas K. ; Utzinger, Jürg ; Vounatsou, Penelope. / Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. In: Geospatial Health. 2013 ; Vol. 7, No. 2. pp. 355-366.

Bibtex

@article{a8282c64e25d4cf5b3adc1d46f8ee036,
title = "Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling",
abstract = "Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8{\%}) covering 31 states, S. mansoni in 78 (16.7{\%}) locations in 22 states and S. intercalatum in 17 (3.7{\%}) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5{\%} and 100{\%} for S. haematobium, 0.2{\%} to 87{\%} for S. mansoni and 1{\%} to 10{\%} for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2{\%} to 75{\%} with a mean prevalence of 23{\%} for the country as a whole (95{\%} confidence interval (CI): 22.8-23.1{\%}). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is",
keywords = "LIFE, Schistosomiasis, Prevalense, goe-referencing, geographical information system, risk mapping, Bayesian geospatial modelling, control, Nigeria",
author = "Ekpo, {Uwem F.} and Eveline H{\"u}rlimann and Nadine Schur and Oluwole, {Akinola S.} and Abe, {Eniola M.} and Mafe, {Margaret A.} and Nebe, {Obiageli J.} and Sunday Isiyaku and Francisca Olamiju and Mukaila Kadiri and Poopola, {Temitope O. S.} and Braide, {Eka I.} and Yisa Saka and Mafiana, {Chiedu F.} and Kristensen, {Thomas K.} and J{\"u}rg Utzinger and Penelope Vounatsou",
year = "2013",
language = "English",
volume = "7",
pages = "355--366",
journal = "Geospatial Health",
issn = "1827-1987",
publisher = "Pagepress",
number = "2",

}

RIS

TY - JOUR

T1 - Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

AU - Ekpo, Uwem F.

AU - Hürlimann, Eveline

AU - Schur, Nadine

AU - Oluwole, Akinola S.

AU - Abe, Eniola M.

AU - Mafe, Margaret A.

AU - Nebe, Obiageli J.

AU - Isiyaku, Sunday

AU - Olamiju, Francisca

AU - Kadiri, Mukaila

AU - Poopola, Temitope O. S.

AU - Braide, Eka I.

AU - Saka, Yisa

AU - Mafiana, Chiedu F.

AU - Kristensen, Thomas K.

AU - Utzinger, Jürg

AU - Vounatsou, Penelope

PY - 2013

Y1 - 2013

N2 - Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is

AB - Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is

KW - LIFE

KW - Schistosomiasis

KW - Prevalense

KW - goe-referencing

KW - geographical information system

KW - risk mapping

KW - Bayesian geospatial modelling

KW - control

KW - Nigeria

M3 - Journal article

VL - 7

SP - 355

EP - 366

JO - Geospatial Health

JF - Geospatial Health

SN - 1827-1987

IS - 2

ER -

ID: 46069064