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POGGIO Thelma Veronica
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MODELLING DIAGNOSTICS FOR ECHINOCOCCUS GRANULOSUS SURVEILLANCE IN SHEEP USING LATENT CLASS ANALYSIS: ARGENTINA AS A CASE STUDY
POGGIO, THELMA VERÓNICA; EDMUNDO LARRIEU; ABAGAEL L. SYKES; BASAÑEZ MARIA GLORIA; PRADA JOAQUIN; CESPEDES GRACIELA
Congreso; 10th National and 3rd International Congress of Hydatidology; 2022
Turkish Society of Hydatidology
MODELLING DIAGNOSTICS FOR ECHINOCOCCUS GRANULOSUS SURVEILLANCE IN SHEEP USING LATENT CLASS ANALYSIS: ARGENTINA AS A CASE STUDYAbagael L. Sykesa, Edmundo Larrieub,c, Thelma Verónica Poggiod, M. Graciela Céspedese, Guillermo B. Mujicaf, Maria-GloriaBasáñeza,1, Joaquin M.Pradag,1a London Centre for Neglected Tropical Disease Research and MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UKb Facultad de Ciencias Veterinarias, Universidad Nacional de La Pampa, General Pico, Argentinac Escuela de Veterinaria, Universidad Nacional de Río Negro, Choele Choel, Argentinad Instituto de Ciencia y Tecnología César Milstein (CONICET), Buenos Aires, Argentinae Instituto Nacional de Enfermedades Infecciosas, Buenos Aires, Argentinaf Ministerio de Salud, Provincia de Río Negro, Viedma, Argentinag Faculty of Health and Medical Sciences, University of Surrey, Guildford, UKAbstract:Objectives: Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with medical and financial impacts, whose reduction requires the application of a One Health approach. However, a lack of accurate and practical diagnostics in livestock impedes the assessment of animal disease burden and the implementation and evaluation of control strategies. Materials and Methods: We use a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep from the Río Negro province of Argentina, accounting for uncertainty in the diagnostics. Model outputs were used to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (the assigned LCA latent variable). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. Results: The estimated prevalence of ovine CE was 27.5% (95% Bayesian credible interval (95%BCI): 13.8%?58.9%) within the sample population. At the individual level, using an optical density (OD) threshold of 0.378, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%?68%) and 68% (95%BCI: 63%?92%), respectively. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal OD threshold of 0.496. Conclusion: These results suggest that the novel ELISA could be beneficial as a flock-level diagnostic for CE surveillance in the region, supplementing human-focused surveillance activities and strengthening a One Health approach. Keywords: Echinococcosis; Surveillance; Latent class analysis; Bayesian inference; Diagnostics.