A multi-agent simulation to assess the risk of malaria re-emergence in southern France

Catherine Linard, Nicolas Ponçon, Didier Fontenille, Eric F. Lambin

Research output: Contribution to journalArticlepeer-review


A multi-agent simulation (MAS) was developed to assess the risk of malaria re-emergence in the Camargue in southern France, a non-endemic area where mosquitoes of the genus Anopheles (Culicidae) live. The contact rate between people and potential malaria vectors, or the human biting rate, is one of the key factor to predict the risk of re-emergence of malaria, would the parasite be introduced in the region. Our model (called MALCAM) represents the different agents that could influence malaria transmission in the Camargue - people, mosquitoes, animal hosts and the landscape - in a spatially explicit environment. The model simulates spatial and temporal variations in human biting rate at the landscape scale. These variations depend on the distribution of people and potential vectors, their behaviour and their interactions. A land use/cover map was used as a cellular-spatial support for the movements of and interactions between mobile agents. The model was tested for its sensitivity to variations in parameter values, and for the agreement between field observations and model predictions. The MALCAM model provides a tool to better understand the interactions between the multiple agents of the disease transmission system, and the land use and land cover factors that control the spatial heterogeneity in these interactions. It allows testing hypotheses and scenarios related to disease dynamics by varying the value of exogenous biological, geographical, or human factors. This application of agent-based modelling to a human vector-borne disease can be adapted to different diseases and regions.

Original languageEnglish
Pages (from-to)160-174
Number of pages15
JournalEcological Modelling
Issue number2
Publication statusPublished - 24 Jan 2009
Externally publishedYes


  • Anopheles hyrcanus
  • Camargue
  • Human biting rate
  • Land use
  • Malaria
  • Multi-agent simulation


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