Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa

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Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa. / Stanton, Michelle; Agier, Agier; Taylor, Benjamin et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 177, No. 3, 06.2014, p. 661-678.

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

Harvard

Stanton, M, Agier, A, Taylor, B & Diggle, P 2014, 'Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 177, no. 3, pp. 661-678. https://doi.org/10.1111/rssa.12033

APA

Stanton, M., Agier, A., Taylor, B., & Diggle, P. (2014). Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa. Journal of the Royal Statistical Society: Series C (Applied Statistics), 177(3), 661-678. https://doi.org/10.1111/rssa.12033

Vancouver

Stanton M, Agier A, Taylor B, Diggle P. Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2014 Jun;177(3):661-678. Epub 2013 Nov 4. doi: 10.1111/rssa.12033

Author

Stanton, Michelle ; Agier, Agier ; Taylor, Benjamin et al. / Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2014 ; Vol. 177, No. 3. pp. 661-678.

Bibtex

@article{4284c26e6436410c9c5db8e289ac8ec8,

title = "Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa",

abstract = "Within an area of sub-Saharan Africa termed {\textquoteleft}the meningitis belt{\textquoteright}, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy currently utilised is reactive, such that a vaccination programme is initiated in a district once a pre-defined weekly incidence threshold is exceeded. In this paper we report progress towards the development of an early warning system based on statistical modelling of district-level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared usingweekly epidemiological data from Niger for the period 1986-2007. We conclude that the models under consideration are advantageous in different situations. The described three-state Markov model in which observed incidence is categorised according to policy-defined thresholds gives the most reliable short term forecasts, whereas the proposed dynamic linear model, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.",

keywords = "Dynamic generalized linear models, Epidemic control , Markov chain , Meningitis belt , Meningococcal meningitis",

author = "Michelle Stanton and Agier Agier and Benjamin Taylor and Peter Diggle",

year = "2014",

month = jun,

doi = "10.1111/rssa.12033",

language = "English",

volume = "177",

pages = "661--678",

journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",

issn = "0035-9254",

publisher = "Wiley-Blackwell",

number = "3",

}

RIS

TY - JOUR

T1 - Towards real-time spatio-temporal prediction of district-level meningitis incidence in sub-Saharan Africa

AU - Stanton, Michelle

AU - Agier, Agier

AU - Taylor, Benjamin

AU - Diggle, Peter

PY - 2014/6

Y1 - 2014/6

N2 - Within an area of sub-Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy currently utilised is reactive, such that a vaccination programme is initiated in a district once a pre-defined weekly incidence threshold is exceeded. In this paper we report progress towards the development of an early warning system based on statistical modelling of district-level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared usingweekly epidemiological data from Niger for the period 1986-2007. We conclude that the models under consideration are advantageous in different situations. The described three-state Markov model in which observed incidence is categorised according to policy-defined thresholds gives the most reliable short term forecasts, whereas the proposed dynamic linear model, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.

AB - Within an area of sub-Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy currently utilised is reactive, such that a vaccination programme is initiated in a district once a pre-defined weekly incidence threshold is exceeded. In this paper we report progress towards the development of an early warning system based on statistical modelling of district-level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared usingweekly epidemiological data from Niger for the period 1986-2007. We conclude that the models under consideration are advantageous in different situations. The described three-state Markov model in which observed incidence is categorised according to policy-defined thresholds gives the most reliable short term forecasts, whereas the proposed dynamic linear model, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.

KW - Dynamic generalized linear models

KW - Epidemic control

KW - Markov chain

KW - Meningitis belt

KW - Meningococcal meningitis

U2 - 10.1111/rssa.12033

DO - 10.1111/rssa.12033

M3 - Journal article

VL - 177

SP - 661

EP - 678

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 3

ER -

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