000095100 001__ 95100
000095100 005__ 20230914083304.0
000095100 0247_ $$2doi$$a10.3389/fvets.2020.00253
000095100 0248_ $$2sideral$$a118676
000095100 037__ $$aART-2020-118676
000095100 041__ $$aeng
000095100 100__ $$0(orcid)0000-0002-1204-4356$$ade Blas, Ignacio$$uUniversidad de Zaragoza
000095100 245__ $$aAssessment of sample size calculations used in aquaculture by simulation techniques
000095100 260__ $$c2020
000095100 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095100 5203_ $$aAn adequate sampling methodology is the key to knowing the health status of aquatic populations. Usually, the aims of epidemiological surveys in aquaculture are to detect an infection and estimate the disease prevalence, and different formulas are used to calculate the sample size. The main objective of this study was to assess if the sample sizes calculated using classical epidemiological formulas are valid considering the sampling methodology, the population size, and the spatial distribution of diseased animals in the population (non-clustered or clustered). However, the use of sample sizes of 30, 60, and 150 fish is widely accepted in aquaculture, due to the requirements of the World Organization for Animal Health (OIE) for epidemiological surveillance. We have developed a specific software using ASP (Active Server Pages) language and MySQL database in order to generate aquatic populations from 100 to 10 000 brown trouts infected by Aeromonas salmonicida with different levels of prevalence: 2, 5, 10, and 50%. Then we implemented several Monte Carlo simulations to estimate empirically the sample sizes corresponding to the different scenarios. Furthermore, we compared these results with the values calculated by classical formulas. We determined that simple random sampling was more accurate in detecting an infection, because it is independent of the distribution of infected animals in the population. However, if diseased animals are non-clustered it is more efficient to use systematic methods, even in the case of small populations. Finally, the formula to calculate sample size to estimate disease prevalence is not valid when the expected prevalence is far from 50%, and it is necessary to increase the sample size to reach the desired precision.
000095100 536__ $$9info:eu-repo/grantAgreement/ES/DGA/A17-17R
000095100 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095100 590__ $$a3.412$$b2020
000095100 591__ $$aVETERINARY SCIENCES$$b9 / 146 = 0.062$$c2020$$dQ1$$eT1
000095100 592__ $$a0.877$$b2020
000095100 593__ $$aVeterinary (miscellaneous)$$c2020$$dQ1
000095100 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095100 700__ $$0(orcid)0000-0003-3074-5778$$aMuniesa, Ana$$uUniversidad de Zaragoza
000095100 700__ $$aVallejo, Adriana
000095100 700__ $$0(orcid)0000-0002-6314-6395$$aRuiz-Zarzuela, Imanol$$uUniversidad de Zaragoza
000095100 7102_ $$11009$$2773$$aUniversidad de Zaragoza$$bDpto. Patología Animal$$cÁrea Sanidad Animal
000095100 773__ $$g7, 253 (2020), 1-9$$pFront. vet. sci.$$tFrontiers in Veterinary Science$$x2297-1769
000095100 8564_ $$s3094712$$uhttps://zaguan.unizar.es/record/95100/files/texto_completo.pdf$$yVersión publicada
000095100 8564_ $$s30175$$uhttps://zaguan.unizar.es/record/95100/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095100 909CO $$ooai:zaguan.unizar.es:95100$$particulos$$pdriver
000095100 951__ $$a2023-09-13-10:55:27
000095100 980__ $$aARTICLE