First expert elicitation of knowledge on drivers of emergence of the COVID-19 in pets

Abstract

Infection with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) induces the coronavirus infectious disease 19 (COVID-19). Its pandemic form in human population and its probable animal origin, along with recent case reports in pets, make drivers of emergence crucial in domestic carnivore pets, especially cats, dogs and ferrets. Few data are available in these species; we first listed forty-six possible drivers of emergence of COVID-19 in pets, regrouped in eight domains (i.e. pathogen/disease characteristics, spatial-temporal distance of outbreaks, ability to monitor, disease treatment and control, characteristics of pets, changes in climate conditions, wildlife interface, human activity, and economic and trade activities). Secondly, we developed a scoring system per driver, then elicited scientific experts (N = 33) to: (a) allocate a score to each driver, (b) weight the drivers scores within each domain and (c) weight the different domains between them. Thirdly, an overall weighted score per driver was calculated; drivers were ranked in decreasing order. Fourthly, a regression tree analysis was used to group drivers with comparable likelihood to play a role in the emergence of COVID-19 in pets. Finally, the robustness of the expert elicitation was verified. Five drivers were ranked with the highest probability to play a key role in the emergence of COVID-19 in pets: availability and quality of diagnostic tools, human density close to pets, ability of preventive/control measures to avoid the disease introduction or spread in a country (except treatment, vaccination and reservoir(s) control), current species specificity of the disease-causing agent and current knowledge on the pathogen. As scientific knowledge on the topic is scarce and still uncertain, expert elicitation of knowledge, in addition with clustering and sensitivity analyses, is of prime importance to prioritize future studies, starting from the top five drivers. The present methodology is applicable to other emerging pet diseases.

Keywords: COVID-19; SARS-CoV-2; carnivores; clustering analysis; drivers; expert elicitation; pets; sensitivity analysis.

FIGURE 1

Cumated frequency of pet‐related and non‐pet‐related COVID‐19 publications, in PubMed [a] and ProMED‐mail [b]. All‐pets = all publications on COVID‐19 without pet‐related publications; Pets = exclusively pet‐related publications on COVID‐19. Numbers represent the cumulated frequency for pet‐related publications

FIGURE 2

Boxplot of the relative importance of the eight domains of COVID‐19 drivers of emergence in pets (N = 33 experts). The dashed line represents the median of the score distribution between the different experts; the solid lines below and above each rectangle represent, respectively, the first and the third quartiles; adjacent lines to the whiskers represent the limits of the 95% confidence interval; small circles represent outside values. The eight domains of drivers are as follows: D1, pathogen/disease characteristics; D2, distance of outbreaks (spatial‐temporal scales); D3, ability to monitor, treat and control the disease; D4, pets characteristics; D5, changes in climate conditions; D6, wildlife interface; D7, human activity; and D8, economic and trade activities

FIGURE 3

Ranking of the overall weighted score for each potential COVID‐19 driver of emergence in pets (Boxplot based on 33 experts). X‐axis represents the drivers with the following codification: D1 to D8 refer to the eight domains of drivers and D1_1 to D8_5 refer to a specific driver (for the codification, see Table 1). Relation to Figure 4 was provided by the group named as, respectively, ‘very high’, ‘high’, ‘moderate’ and ‘low’ importance of the COVID‐19 drivers of emergence in pets

FIGURE 4

Aggregation of COVID‐19 drivers of emergence in pets into four homogenous groups using a regression tree analysis. N, number; SD, standard deviation

FIGURE 5

Sensitivity analysis according to the experts. The diagram visualizes any modification in the rank of COVID‐19 drivers of emergence in pets induced by the withdrawal of a given expert's input. X, crosses represent the cut‐off of more than five ranks between different steps. Withdrawal of experts have little effect on the ranking. X‐axis represents the expert considered: All, all experts; All‐Exp1 to All‐Exp33 all, experts minus the first (Exp1), the second (Exp2), until the last (Exp33). Y‐axis represents the ranking of the COVID‐19 drivers of emergence in pets, which are presented in Table 1 (i.e. the domain code followed by driver code). Several drivers occupy the same rank because their overall weighted scores are similar