title: A model to estimate the optimal sample size for microbiological surveys.
abstract: Estimating optimal sample size for microbiological surveys is a challenge for laboratory managers. When insufficient sampling is conducted, biased inferences are likely; however, when excessive sampling is conducted valuable laboratory resources are wasted. This report presents a statistical model for the estimation of the sample size appropriate for the accurate identification of the bacterial subtypes of interest in a specimen. This applied model for microbiology laboratory use is based on a Bayesian mode of inference, which combines two inputs: (ii) a prespecified estimate, or prior distribution statement, based on available scientific knowledge and (ii) observed data. The specific inputs for the model are a prior distribution statement of the number of strains per specimen provided by an informed microbiologist and data from a microbiological survey indicating the number of strains per specimen. The model output is an updated probability distribution of strains per specimen, which can be used to estimate the probability of observing all strains present according to the number of colonies that are sampled. In this report two scenarios that illustrate the use of the model to estimate bacterial colony sample size requirements are presented. In the first scenario, bacterial colony sample size is estimated to correctly identify Campylobacter amplified restriction fragment length polymorphism types on broiler carcasses. The second scenario estimates bacterial colony sample size to correctly identify Salmonella enterica serotype Enteritidis phage types in fecal drag swabs from egg-laying poultry flocks. An advantage of the model is that as updated inputs from ongoing surveys are incorporated into the model, increasingly precise sample size estimates are likely to be made.
authors.affiliation: Center for Veterinary Medicine, Food and Drug Administration, Rockville, Maryland, USA. sean.altekruse@fsis.usda.gov
authors.forename: S F
authors.initials: SF
authors.lastname: Altekruse
authors.forename: F
authors.initials: F
authors.lastname: Elvinger
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: K
authors.initials: K
authors.lastname: Ye
mesh_terms: D000818:Animals; D001434:Bacteriophage Typing; D001499:Bayes Theorem; D002167:Campylobacter / Q000145:classification / Q000235:genetics / Q000302:isolation & purification*; D002169:Campylobacter Infections / Q000662:veterinary; D002645:Chickens; D005243:Feces / Q000821:virology*; D007753:Laboratories; D008829:Microbiology; D008954:Models, Biological; D012150:Polymorphism, Restriction Fragment Length; D011201:Poultry Diseases / Q000382:microbiology; D011336:Probability; D012483:Salmonella Phages / Q000145:classification / Q000235:genetics / Q000302:isolation & purification*; D012477:Salmonella enteritidis / Q000821:virology*; D018401:Sample Size; D012494:Sampling Studies