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    Rights statement: This is the peer reviewed version of the following article:Giorgi E, Schlüter DK, Diggle PJ. Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection. Environmetrics. 2017;e2447. https://doi.org/10.1002/env.2447 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/env.2447/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>24/05/2017
<mark>Journal</mark>Environmetrics
Number of pages10
Publication statusE-pub ahead of print
Early online date24/05/17
Original languageEnglish

Abstract

Loiasis is a neglected tropical disease (NTD) caused by the parasitic roundworm Loa loa. A challenge faced by current multinational programmes to control two other diseases, namely, lymphatic filariasis and onchocerciasis, by mass administration of prophylactic medication to at-risk communities is that individuals highly coinfected with Loa loa are at risk of developing serious adverse reactions to the medication. For this reason, understanding the geographical distribution of Loa loa prevalence and the distribution of microfilarial loads in communities has become of crucial importance. In this paper, we develop methodology to analyse data on microfilariae counts per millilitre of blood whilst allowing for spatial correlation. One feature of the data is the excess of zero counts, which makes the use of standard geostatistical methods for prevalence data inappropriate. This phenomenon, also known as zero inflation, is typical of count data from NTDs, whose endemic boundaries are often unknown, thus leading to the inclusion of disease-free communities in the sampling frame. We introduce a bivariate geostatistical model in order to study the relationship between the distributions of prevalence and intensity of Loa loa infections at the community level. We show through a simulation study that the spatial model leads to more precise spatial predictions than the nonspatial approach used by Schlüter et al. (2016), and accordingly provide a geostatistical reanalysis of the Loa loa data.

Bibliographic note

This is the peer reviewed version of the following article:Giorgi E, Schlüter DK, Diggle PJ. Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection. Environmetrics. 2017;e2447. https://doi.org/10.1002/env.2447 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/env.2447/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.