First expert elicitation of knowledge on drivers of emergence of influenza D in Europe

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Saegerman, Claude | Bianchini, Juana | Snoeck, Chantal, J. | Moreno, Ana | Chiapponi, Chiara | Zohari, Siamak | Ducatez, Mariette, F.

Edité par CCSD ; Wiley-Blackwell -

International audience. The influenza D virus (IDV) was first identified and characterized in 2011. Considering the virus' zoonotic potential, its genome nature (segmented RNA virus), its worldwide circulation in livestock and its role in bovine respiratory disease, an increased interest is given to IDV. However, few data are available on drivers of emergence of IDV. We first listed fifty possible drivers of emergence of IDV in ruminants and swine. As recently carried out for COVID-19 in pets (Transboundary and Emerging Diseases, 2020), a scoring system was developed per driver and scientific experts (N = 28) were elicited to (a) allocate a score to each driver, (b) weight the drivers' scores within each domain and (c) weight the different domains among themselves. An overall weighted score was calculated per driver, and drivers were ranked in decreasing order. Drivers with comparable likelihoods to play a role in the emergence of IDV in ruminants and swine in Europe were grouped using a regression tree analysis. Finally, the robustness of the expert elicitation was verified. Eight drivers were ranked with the highest probability to play a key role in the emergence of IDV: current species specificity of the causing agent of the disease; influence of (il)legal movements of live animals (ruminants, swine) from neighbouring/European Union member states and from third countries for the disease to (re-)emerge in a given country; detection of emergence; current knowledge of the pathogen; vaccine availability; animal density; and transport vehicles of live animals. As there is still limited scientific knowledge on the topic, expert elicitation of knowledge and multi-criteria decision analysis, in addition to clustering and sensitivity analyses, are very important to prioritize future studies, starting from the top eight drivers. The present methodology could be applied to other emerging animal diseases.

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