Abstract
An 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.
Highlights
Epidemiological surveillance in aquatic populations aims to assess the risk of the introduction and spreading of pathogens (1), a balanced relationship cost-benefit is required.One of the key elements of a surveillance program is the sampling method, and it should warrant the representativity of the results (2)
A positive value indicates that sample size estimated by simulation is greater than the sample size calculated with the formula
Non-clustered 1 cluster 3 clusters 5 clusters underestimates the required sample size); on the other hand, a negative value indicates that sample size estimated by simulation is lower than the sample size calculated with the formula
Summary
One of the key elements of a surveillance program is the sampling method, and it should warrant the representativity of the results (2). The sample size varies considerably depending on the expected results, since the goals of surveillance are usually pathogen detection and prevalence estimation (3, 4). Frontiers in Veterinary Science | www.frontiersin.org de Blas et al. Sample Size Calculations by Simulation Techniques. The detection of a specific pathogen is the main objective of the surveillance programs for notifiable diseases (5), and in this case, the limiting factor is the collection of a sufficient number of samples (4). A non-probability sampling method is used, and the sample size is directly related to expected prevalence (design prevalence), so the higher the prevalence is, the more chance to find an infected animal, and the required sample size is lower (6)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
