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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

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Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection. / Giorgi, Emanuele; Schlüter, Daniela K; Diggle, Peter John.

In: Environmetrics, 24.05.2017.

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@article{3b2ec12fbcc740d0a234f6272ce89092,
title = "Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection",
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 Schl{\"u}ter et al. (2016), and accordingly provide a geostatistical reanalysis of the Loa loa data.",
keywords = "disease mapping, geostatistics, Loa loa, neglected tropical diseases, spatial correlation, zero inflation",
author = "Emanuele Giorgi and Schl{\"u}ter, {Daniela K} and Diggle, {Peter John}",
note = "This is the peer reviewed version of the following article:Giorgi E, Schl{\"u}ter 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.",
year = "2017",
month = may,
day = "24",
doi = "10.1002/env.2447",
language = "English",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",

}

RIS

TY - JOUR

T1 - Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection

AU - Giorgi, Emanuele

AU - Schlüter, Daniela K

AU - Diggle, Peter John

N1 - 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.

PY - 2017/5/24

Y1 - 2017/5/24

N2 - 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.

AB - 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.

KW - disease mapping

KW - geostatistics

KW - Loa loa

KW - neglected tropical diseases

KW - spatial correlation

KW - zero inflation

U2 - 10.1002/env.2447

DO - 10.1002/env.2447

M3 - Journal article

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

ER -