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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Bluetongue transmission and control in Europe: A systematic review of compartmental mathematical models
Noémie Courtejoie1, Gina Zanella2, Benoît Durand2
1Epidemiology Unit, Laboratory for Animal Health, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), University Paris-Est, 14 rue Pierre et Marie Curie, Maisons-Alfort, 94700, France; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 28 rue du docteur Roux, Paris, 75015, France.
Abstract:
The growing frequency of bluetongue virus (BTV) incursions in Europe in recent years led to the largest BTV outbreak ever recorded in 2006/09, with a dramatic impact on the cattle and sheep industries. The complex epidemiology of this vector-borne disease of ruminants and its recent emergence need to be better understood to identify and implement efficient control strategies. Mathematical models provide useful tools for that purpose; many of them have been developed in the light of the 2006/09 outbreak. We aimed to provide a systematic review of compartmental mathematical models dedicated to BTV occurrence or transmission in European countries, to assess robustness of findings to different modelling approaches and assumptions. We identified relevant papers from PubMed and Scopus databases, 21 of which were included in the review following the selection process laid out in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. We systematically extracted data from these papers to address the diversity and evolution of modelling approaches, and to identify important characteristics for future model development. Then, we summarized the main insights provided into bluetongue epidemiology, and discussed the relevance of these models as tools for risk mapping and for the design of surveillance and control systems. On the whole, the mechanistic models reviewed provided flexible frameworks, yielding mostly epidemiological insights specific to geographical areas and study periods. Despite the limitations of these models that sometimes relied on strong assumptions, we advocate their use to facilitate and inform evidence-based decision-making in animal health.
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