Towards realtime spatiotemporal prediction of district level meningitis incidence in sub-Saharan Africa

Stanton, Michelle ORCID: https://orcid.org/0000-0002-1754-4894, Agier, and Lydiane, Taylor, Benjamin M. and Diggle, Peter J. (2014) 'Towards realtime spatiotemporal prediction of district level meningitis incidence in sub-Saharan Africa'. Journal of the Royal Statistical Society Series A (Statistics in Society), Vol 177, Issue 3, pp. 661-678.

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Abstract

Within an area of sub-Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy that is currently utilized is reactive, such that a vaccination programme is initiated in a district once a predefined weekly incidence threshold has been exceeded. 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 by using weekly epidemiological data from Niger for the period 1986–2007. We conclude that the models under consideration are advantageous in different situations. The three-state Markov model described in which observed incidence is categorized according to policy-defined thresholds gives the most reliable short-term forecasts, whereas the dynamic linear model proposed, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.

Item Type:	Article
Subjects:	WA Public Health > Preventive Medicine > WA 110 Prevention and control of communicable diseases. Transmission of infectious diseases WA Public Health > Preventive Medicine > WA 115 Immunization WA Public Health > WA 30 Socioeconomic factors in public health (General) WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries WC Communicable Diseases > Infection. Bacterial Infections > Bacterial Infections > WC 245 Meningococcal infections
Faculty: Department:	Biological Sciences > Department of Tropical Disease Biology
Digital Object Identifer (DOI):	https://doi.org/10.1111/rssa.12033
Depositing User:	Lynn Roberts-Maloney
Date Deposited:	18 May 2015 10:38
Last Modified:	06 Feb 2018 13:09
URI:	https://archive.lstmed.ac.uk/id/eprint/5149

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