Research

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali

Jean Gaudart^1* , Ousmane Touré^2* , Nadine Dessay³ , A lassane Dicko² , Stéphane Ranque⁴ , Loic Forest^{5^} , Jacques Demongeot⁶ and Ogobara K Doumbo²

¹  Biostatistics Research Unit, Laboratory of Education and Research in Medical Information Processing (LERTIM), EA 3283 Aix-Marseille University, Faculty of Medicine, 27 Bd Jean Moulin, 13385 Marseille cedex 5, France

²  Malaria Research and Training Centre (MRTC), Department of Epidemiology of Parasitic Diseases, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Mali, BP 1805 Bamako, Mali

³  Laboratory of Hydrology Transfers and Environment (LTHE), Domaine Universitaire, 38400 Saint Martin d'Hères, France

⁴  Laboratory of Parasitology-Mycology, Hôpital de La Timone, AP-HM, 13005 Marseille, France

⁵  INSA Rouen, Laboratory of Mathematics and informatics EA3226, Place Emile Blondel, BP 08, 76131 Mont Saint-Aignan, France

⁶  University Joseph Fourier Grenoble, Laboratory of Techniques for Imaging, Modelling and Complexity – Informatics, Mathematics and Applications Grenoble, TIMC-IMAG UMR NRS 5525, Faculty of Medicine, Domaine de la Merci, 38710 La Tronche, France

author email corresponding author email^Deceased * Contributed equally

Malaria Journal 2009, 8:61doi:10.1186/1475-2875-8-61

Published:	10 April 2009

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 for a monthly model of environmental changes.

Conclusion

Remote-sensed data were coupled with field study data in order to drive a malaria transmission model. Several studies have shown that the NDVI presents significant correlations with climate variables, such as precipitations particularly in Sudanese savannah environments. Non-linear model combining environmental variables, predisposition factors and transmission pattern can be used for community level risk evaluation.