Estimation of infection prevalence from correlated binomial samples

Preventive Veterinary Medicine
J CondonN Leonard

Abstract

Infection prevalence in a population often is estimated from grouped binary data expressed as proportions. The groups can be families, herds, flocks, farms, etc. The observed number of cases generally is assumed to have a Binomial distribution and the estimate of prevalence is then the sample proportion of cases. However, the individual binary observations might not be independent--leading to overdispersion. The goal of this paper was to demonstrate random-effects models for the estimation of infection prevalence from data which are correlated and in particular, to illustrate a nonparametric random-effects model for this purpose. The nonparametric approach is a relatively recent addition to the random-effects class of models and does not appear to have been discussed previously in the veterinary epidemiology literature. The assumptions for a logistic-regression model with a nonparametric random effect were outlined. In a demonstration of the method on data relating to Salmonella infection in Irish pig herds, the nonparametric method resulted in the classification of herds into a small number of distinct prevalence groups (i.e. low, medium and high prevalence) and also estimated the relative frequency of each prevalence category...Continue Reading

Citations

May 12, 2009·Epidemiology and Infection·I H M FriesemaY T H P van Duynhoven
Jul 29, 2009·PloS One·Victoriya V VolkovaRobert W Wills
Mar 2, 2012·Emerging Infectious Diseases·Margo E Chase-ToppingDavid L Gally
Sep 3, 2010·Zoonoses and Public Health·V V VolkovaW W Robert

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