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How network analysis of oyster movements can improve surveillance and control programs of infectious diseases?

Résumé

Animal movements are one of the main ways to introduce and spread pathogens. In French oyster farming, many stakeholders and premises are heterogeneously divided over the country and a highly dynamic flow of oysters exists among them. In the context of animal disease surveillance and control, analysis of the animal movement’ network can provide useful information to build adapted surveillance strategies or to develop risk management. Movement network analysis has been widely used in terrestrial production, to evaluate the vulnerability of animal movement network to the spread of a specific disease. In France, since 2008, Pacific oyster spat (Crassostrea gigas) has been affected by massive mortality outbreaks associated with the detection of a newly reported variant of ostreid herpesvirus type 1 (OsHV-1). These mortality events have a direct economic impact causing considerable concern to oyster farmers. A previous epidemiological study has highlighted the potential role of oyster transfers in the spread of these outbreaks. However, neither mandatory database nor reliable data is publicly available concerning oyster transfers in France. In this context, a field study was carried out in the main oyster production area in France, Charente-Maritime bay, to map oyster movements and to characterize the corresponding network structure related to potential disease spread. Seventy-five oyster farmers were randomly selected in Charente-Maritime bay between July and September 2010, according to a stratified sampling design based on farm category regarding production type and location (i.e. spat producers, local farmers, beyond farmers, local farmer-senders, beyond farmer-senders). Data related to the farm characteristics and activities, routine rearing scheme and potential changes in husbandry practices were collected during a face-to-face interview of the oyster farmer, using a standardized questionnaire and a land register. Movement data were spatialized and analyzed using social network analysis. A 85.3% participation rate to the study was obtained. Results showed a seasonal variation of oyster transfers in all the sampling strata, with peaks of transfers in spring and autumn, and different geographical patterns of oyster transfers between strata. Growing sites were shared by farms belonging to different farm category which have different behaviour related to oyster movements in time. These results show heterogeneity of contacts between strata both in time and space. Network measures allowed identifying the growing sites having a high risk of becoming infected, of transmitting diseases to other sites or controlling the flow of oysters from one part of the network to another part. The growing sites at high risk of becoming infected could be targeted for surveillance activities (Figure 1). The growing sites at high risk of transmitting diseases to other sites could be the first ones where implementation of early or enhanced control measures (e.g. quarantine, depopulation) could be focused. The growing sites controlling the flow of oysters could be considered as key players in the initial spread of a disease. If these sites would be removed from the network by early movement restriction, this may contribute to contain the disease spread in a smaller part of the area, providing that the propagation by the marine currents and the effect of environmental factors are considered all together. As a conclusion, movement network analysis may provide valuable information to policy makers in preparedness (surveillance) and response (control) to an epidemic.

Dates et versions

hal-01605650 , version 1 (02-10-2017)

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Citer

Coralie Lupo, Pauline Ezanno, Isabelle Arzul, Céline Garcia, Cécile Jadot, et al.. How network analysis of oyster movements can improve surveillance and control programs of infectious diseases?. 1. AquaEpi (Aquatic Animal Epidemiology), Sep 2016, Oslo, Norway. , 2016, 1. AquaEpi (Aquatic Animal Epidemiology). ⟨10.3389/conf.FVETS.2016.02.00044⟩. ⟨hal-01605650⟩
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