Research
A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
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* Corresponding author: Jean-Marc O Depinay depinayj@mail.nih.gov
1 Fogarty International Center, National Institutes of Health, 16 Center Drive, Bethesda MD 20892, USA
2 Kenya Medical Research Institute, Centre for Geographic Medicine Research – Coast, P.O. Box 428, Kilifi, Kenya
3 International Centre of Insect Physiology and Ecology, P.O. Box 30772, Nairobi, Kenya
4 Ifakara Health Research and Development Centre, PO Box 53 Ifakara, Kilombero District, Tanzania
5 Entomology Unit, FAO/IAEA Agriculture and Biotechnology Laboratory, A-2444 Seibersdorf, Austria
6 Global Public Health Program, University of Miami, South Campus, 12500 SW 152nd Street, Building B, Miami, FL 33177, USA
7 Tulane University, New Orleans, LA 70118, USA
8 University of Aberdeen, Zoology Building, University of Aberdeen, Aberdeen AB24 2TZ, UK
9 School of Mathematics, Statistics and IT, University of Natal, Private Bag X01 Scottsville, 3209 Pietermaritzburg, South Africa
10 Faculty of Medicine, Pharmacy, and Dentistry, Malaria Research and Training Center; B.P. 1805 Bamako, Mali
Malaria Journal 2004, 3:29 doi:10.1186/1475-2875-3-29
Published: 30 July 2004Abstract
Background
Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs.
Methods
This study presents a simulation model of African malaria vectors. This individual-based model incorporates current knowledge of the mechanisms underlying Anopheles population dynamics and their relations to the environment. One of its main strengths is that it is based on both biological and environmental variables.
Results
The model made it possible to structure existing knowledge, assembled in a comprehensive review of the literature, and also pointed out important aspects of basic Anopheles biology about which knowledge is lacking. One simulation showed several patterns similar to those seen in the field, and made it possible to examine different analyses and hypotheses for these patterns; sensitivity analyses on temperature, moisture, predation and preliminary investigations of nutrient competition were also conducted.
Conclusions
Although based on some mathematical formulae and parameters, this new tool has been developed in order to be as explicit as possible, transparent in use, close to reality and amenable to direct use by field workers. It allows a better understanding of the mechanisms underlying Anopheles population dynamics in general and also a better understanding of the dynamics in specific local geographic environments. It points out many important areas for new investigations that will be critical to effective, efficient, sustainable interventions.