Porcine reproductive and respiratory syndrome virus (PRRSv) infection causes a devastating economic impact to the swine industry. Active surveillance is routinely conducted in many swine herds to demonstrate freedom from PRRSv infection. The design of efficient active surveillance sampling schemes is challenging because optimum surveillance strategies may differ depending on infection status, herd structure, management, or resources for conducting sampling. Here, we present an open web-based application, named ‘OptisampleTM’, designed to optimize herd sampling strategies to substantiate freedom of infection considering also costs of testing. In addition to herd size, expected prevalence, test sensitivity, and desired level of confidence, the model takes into account the presumed risk of pathogen introduction between samples, the structure of the herd, and the process to select the samples over time. We illustrate the functionality and capacity of ‘OptisampleTM’ through its application to active surveillance of PRRSv in hypothetical swine herds under disparate epidemiological situations. Diverse sampling schemes were simulated and compared for each herd to identify effective strategies at low costs. The model results show that to demonstrate freedom from disease, it is important to consider both the epidemiological situation of the herd and the sample selected. The approach illustrated here for PRRSv may be easily extended to other animal disease surveillance systems using the web-based application available at http://stemma.ahc.umn.edu/optisample.
Bibliographical noteFunding Information:
This study was funded in part by grants of the University of Minnesota MnDrive program and the US Center of Excellence in Emerging and Zoonotic Animal Diseases (CEEZAD/KBA). We dedicate a very special acknowledgment to Robert E. Morrison for his vision and mentoring. We would like to thank all comments and suggestions of Dr. Evan Sargeant, Tony Martin and Uli Muellner. We thank Andrea Arruda and Carles Vilalta for providing data that support the assumptions of the stated scenarios. We are also very grateful for all the suggestions and corrections proposed by the two anonymous referees.
© 2017 Alba et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.