Use of epidemiologic information in targeted surveillance for population inference

• Published in: Preventive veterinary medicine 2009-05, Vol.89 (1), p.43-50
• Main Authors: Wells, Scott J., Ebel, Eric D., Williams, Michael S., Scott, Aaron E., Wagner, Bruce A., Marshall, Katherine L.
• Format: Article
• Language: English
• Subjects: animal diseases; Animals; Cattle; Cattle Diseases - epidemiology; Coccidiosis - epidemiology; Coccidiosis - veterinary; Computer Simulation; Confidence Intervals; disease detection; disease prevalence; disease surveillance; Epidemiologic Studies; epidemiological studies; estimation; Female; Male; model validation; Monte Carlo Method; neosporosis; Odds Ratio; paratuberculosis; Paratuberculosis - epidemiology; population size; Population Surveillance; prediction; Prevalence estimation; probabilistic models; probability analysis; risk assessment; Risk Factors; Risk ratio; Sample Size; sampling; Sampling Studies; scrapie; Scrapie - epidemiology; Sensitivity and Specificity; Sheep; Targeted surveillance; Uncertainty; United States - epidemiology
• ISSN: 0167-5877; 1873-1716
• DOI: 10.1016/j.prevetmed.2009.01.007

Epidemiologic information, including animal characteristics (e.g., observable risk factors or clinical signs) predisposing to animal disease, is frequently used for design of targeted surveillance systems, but this information is infrequently used for population inference. In this study, we report the evaluation of use of epidemiologic information for population inference in targeted surveillance in three animal disease scenarios. We adapted sampling theory using Monte Carlo methods to determine target population sample size to detect disease with 95% confidence, using information from the epidemiologic parameters risk ratio and fraction of the population with the characteristic. These parameters and their uncertainties were derived from a reference population. The next step was to use a second (sampled) population to evaluate effects of sampling the targeted population. The focus of the study was on estimation of prevalence. Our results showed that if one is less certain of the epidemiologic parameters, a rational decision is to model the input parameter distributions reflecting this uncertainty, thereby increasing the sample size above the minimum needed for the detection of the disease with a known confidence. Targeted surveillance is appropriate for prevalence estimation when one has representative and justifiable estimates of key epidemiologic parameters.