Geo-additive modelling of malaria in Burundi
Abstract Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because...
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ftdoajarticles:oai:doaj.org/article:1f1a422d516e4bed900428be1ee16264 2023-05-15T15:14:59+02:00 Geo-additive modelling of malaria in Burundi Gebhardt Albrecht Nkurunziza Hermenegilde Pilz Jürgen 2011-08-01T00:00:00Z https://doi.org/10.1186/1475-2875-10-234 https://doaj.org/article/1f1a422d516e4bed900428be1ee16264 EN eng BMC http://www.malariajournal.com/content/10/1/234 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-10-234 1475-2875 https://doaj.org/article/1f1a422d516e4bed900428be1ee16264 Malaria Journal, Vol 10, Iss 1, p 234 (2011) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2011 ftdoajarticles https://doi.org/10.1186/1475-2875-10-234 2022-12-30T21:43:24Z Abstract Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. Methods The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. Results The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. Conclusions In this paper, ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 10 1 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Gebhardt Albrecht Nkurunziza Hermenegilde Pilz Jürgen Geo-additive modelling of malaria in Burundi |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. Methods The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. Results The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. Conclusions In this paper, ... |
format |
Article in Journal/Newspaper |
author |
Gebhardt Albrecht Nkurunziza Hermenegilde Pilz Jürgen |
author_facet |
Gebhardt Albrecht Nkurunziza Hermenegilde Pilz Jürgen |
author_sort |
Gebhardt Albrecht |
title |
Geo-additive modelling of malaria in Burundi |
title_short |
Geo-additive modelling of malaria in Burundi |
title_full |
Geo-additive modelling of malaria in Burundi |
title_fullStr |
Geo-additive modelling of malaria in Burundi |
title_full_unstemmed |
Geo-additive modelling of malaria in Burundi |
title_sort |
geo-additive modelling of malaria in burundi |
publisher |
BMC |
publishDate |
2011 |
url |
https://doi.org/10.1186/1475-2875-10-234 https://doaj.org/article/1f1a422d516e4bed900428be1ee16264 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 10, Iss 1, p 234 (2011) |
op_relation |
http://www.malariajournal.com/content/10/1/234 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-10-234 1475-2875 https://doaj.org/article/1f1a422d516e4bed900428be1ee16264 |
op_doi |
https://doi.org/10.1186/1475-2875-10-234 |
container_title |
Malaria Journal |
container_volume |
10 |
container_issue |
1 |
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1766345380232953856 |