Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali
Abstract Background The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have ra...
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ftdoajarticles:oai:doaj.org/article:c5b2e1d3a03142bf9a93e7b5cc50c06f 2023-05-15T15:16:13+02:00 Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali Demongeot Jacques Ranque Stéphane Forest Loic Dicko A lassane Dessay Nadine Touré Ousmane Gaudart Jean Doumbo Ogobara K 2009-04-01T00:00:00Z https://doi.org/10.1186/1475-2875-8-61 https://doaj.org/article/c5b2e1d3a03142bf9a93e7b5cc50c06f EN eng BMC http://www.malariajournal.com/content/8/1/61 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-8-61 1475-2875 https://doaj.org/article/c5b2e1d3a03142bf9a93e7b5cc50c06f Malaria Journal, Vol 8, Iss 1, p 61 (2009) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2009 ftdoajarticles https://doi.org/10.1186/1475-2875-8-61 2022-12-31T04:47:57Z Abstract Background The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models. Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area. Methods A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data. The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia. Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation. Results The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]). The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 8 1 |
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Directory of Open Access Journals: DOAJ Articles |
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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 Demongeot Jacques Ranque Stéphane Forest Loic Dicko A lassane Dessay Nadine Touré Ousmane Gaudart Jean Doumbo Ogobara K Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models. Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area. Methods A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data. The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia. Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation. Results The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]). The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values ... |
format |
Article in Journal/Newspaper |
author |
Demongeot Jacques Ranque Stéphane Forest Loic Dicko A lassane Dessay Nadine Touré Ousmane Gaudart Jean Doumbo Ogobara K |
author_facet |
Demongeot Jacques Ranque Stéphane Forest Loic Dicko A lassane Dessay Nadine Touré Ousmane Gaudart Jean Doumbo Ogobara K |
author_sort |
Demongeot Jacques |
title |
Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
title_short |
Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
title_full |
Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
title_fullStr |
Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
title_full_unstemmed |
Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali |
title_sort |
modelling malaria incidence with environmental dependency in a locality of sudanese savannah area, mali |
publisher |
BMC |
publishDate |
2009 |
url |
https://doi.org/10.1186/1475-2875-8-61 https://doaj.org/article/c5b2e1d3a03142bf9a93e7b5cc50c06f |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 8, Iss 1, p 61 (2009) |
op_relation |
http://www.malariajournal.com/content/8/1/61 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-8-61 1475-2875 https://doaj.org/article/c5b2e1d3a03142bf9a93e7b5cc50c06f |
op_doi |
https://doi.org/10.1186/1475-2875-8-61 |
container_title |
Malaria Journal |
container_volume |
8 |
container_issue |
1 |
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1766346512282943488 |