Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission

Abstract : BACKGROUND : Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems. RESULTS : Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment. CONCLUSION : Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.
Liste complète des métadonnées

Littérature citée [41 références]  Voir  Masquer  Télécharger

https://hal.umontpellier.fr/hal-02392523
Contributeur : Anthony Herrada <>
Soumis le : mercredi 4 décembre 2019 - 09:36:20
Dernière modification le : mardi 11 février 2020 - 10:10:06

Fichier

Guegan_27.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Collections

Citation

Benjamin Roche, Jean-François Guégan, Francois Bousquet. Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission. BMC Bioinformatics, BioMed Central, 2008, 9, pp.435. ⟨10.1186/1471-2105-9-435⟩. ⟨hal-02392523⟩

Partager

Métriques

Consultations de la notice

56

Téléchargements de fichiers

73