Home > Research > Publications & Outputs > A multi-state spatio-temporal Markov model for ...

Research

Links

Text available via DOI:

View graph of relations

A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.

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

Published

Standard

A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa. / Agier, L; Stanton, M; Soga, G et al.
In: Epidemiology and Infection, Vol. 141, No. 8, 01.08.2013, p. 1764-1771.

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

Harvard

Agier, L, Stanton, M, Soga, G & Diggle, PJ 2013, 'A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.', Epidemiology and Infection, vol. 141, no. 8, pp. 1764-1771. https://doi.org/10.1017/S0950268812001926

APA

Agier, L., Stanton, M., Soga, G., & Diggle, PJ. (2013). A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa. Epidemiology and Infection, 141(8), 1764-1771. https://doi.org/10.1017/S0950268812001926

Vancouver

Agier L, Stanton M, Soga G, Diggle PJ. A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa. Epidemiology and Infection. 2013 Aug 1;141(8):1764-1771. doi: 10.1017/S0950268812001926

Author

Agier, L ; Stanton, M ; Soga, G et al. / A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa. In: Epidemiology and Infection. 2013 ; Vol. 141, No. 8. pp. 1764-1771.

Bibtex

@article{2d2628b181e1423f953da469537b41e7,
title = "A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.",
abstract = "Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986–2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65·0%, positive predictive value 49·0%, and an average time gained of 4·6 weeks. These results could inform decisions on preparatory actions.",
author = "L Agier and M Stanton and G Soga and PJ Diggle",
year = "2013",
month = aug,
day = "1",
doi = "10.1017/S0950268812001926",
language = "English",
volume = "141",
pages = "1764--1771",
journal = "Epidemiology and Infection",
issn = "0950-2688",
publisher = "Cambridge University Press",
number = "8",

}

RIS

TY - JOUR

T1 - A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.

AU - Agier, L

AU - Stanton, M

AU - Soga, G

AU - Diggle, PJ

PY - 2013/8/1

Y1 - 2013/8/1

N2 - Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986–2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65·0%, positive predictive value 49·0%, and an average time gained of 4·6 weeks. These results could inform decisions on preparatory actions.

AB - Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986–2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65·0%, positive predictive value 49·0%, and an average time gained of 4·6 weeks. These results could inform decisions on preparatory actions.

UR - http://europepmc.org/abstract/med/22995184

U2 - 10.1017/S0950268812001926

DO - 10.1017/S0950268812001926

M3 - Journal article

C2 - 22995184

VL - 141

SP - 1764

EP - 1771

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 8

ER -