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Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models

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Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. / Alegana, Victor A.; Atkinson, Peter M.; Wright, Jim A. et al.
In: Spatial and Spatio-temporal Epidemiology, Vol. 7, 12.2013, p. 25-36.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Alegana, VA, Atkinson, PM, Wright, JA, Kamwi, R, Uusiku, P, Katokele, S, Snow, RW & Noor, AM 2013, 'Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models', Spatial and Spatio-temporal Epidemiology, vol. 7, pp. 25-36. https://doi.org/10.1016/j.sste.2013.09.001

APA

Alegana, V. A., Atkinson, P. M., Wright, J. A., Kamwi, R., Uusiku, P., Katokele, S., Snow, R. W., & Noor, A. M. (2013). Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. Spatial and Spatio-temporal Epidemiology, 7, 25-36. https://doi.org/10.1016/j.sste.2013.09.001

Vancouver

Alegana VA, Atkinson PM, Wright JA, Kamwi R, Uusiku P, Katokele S et al. Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. Spatial and Spatio-temporal Epidemiology. 2013 Dec;7:25-36. doi: 10.1016/j.sste.2013.09.001

Author

Alegana, Victor A. ; Atkinson, Peter M. ; Wright, Jim A. et al. / Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. In: Spatial and Spatio-temporal Epidemiology. 2013 ; Vol. 7. pp. 25-36.

Bibtex

@article{be26729df11c435791310614c12b341f,
title = "Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models",
abstract = "As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.",
keywords = "Namibia, Malaria, Spatio-temporal, Conditional-autoregressive",
author = "Alegana, {Victor A.} and Atkinson, {Peter M.} and Wright, {Jim A.} and Richard Kamwi and Petrina Uusiku and Stark Katokele and Snow, {Robert W.} and Noor, {Abdisalan M.}",
year = "2013",
month = dec,
doi = "10.1016/j.sste.2013.09.001",
language = "English",
volume = "7",
pages = "25--36",
journal = "Spatial and Spatio-temporal Epidemiology",
issn = "1877-5845",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models

AU - Alegana, Victor A.

AU - Atkinson, Peter M.

AU - Wright, Jim A.

AU - Kamwi, Richard

AU - Uusiku, Petrina

AU - Katokele, Stark

AU - Snow, Robert W.

AU - Noor, Abdisalan M.

PY - 2013/12

Y1 - 2013/12

N2 - As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.

AB - As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.

KW - Namibia

KW - Malaria

KW - Spatio-temporal

KW - Conditional-autoregressive

U2 - 10.1016/j.sste.2013.09.001

DO - 10.1016/j.sste.2013.09.001

M3 - Journal article

VL - 7

SP - 25

EP - 36

JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

SN - 1877-5845

ER -