Evaluation of sampling methods for effective detection of infected pig farms during a disease outbreak

Yoshinori Murato, Yoko Hayama, Yumiko Shimizu, Kotaro Sawai and Takehisa Yamamoto

PLOS ONE, 2020, vol. 15, issue 10, 1-11

Abstract: Emergency surveillance following an outbreak of transboundary animal diseases such as classical swine fever (CSF), is conducted to find another new infection as early as possible. Although larger sample sizes can help achieve higher disease surveillance sensitivity, the sample size is limited by the availability of resources in an emergency situation. Moreover, the recent CSF outbreak reported in Japan was associated with fewer clinical signs; this emphasizes the importance of detecting infected farms by surveillance. In this study, we aimed to identify effective and labor-efficient sampling methods showing high probabilities of detecting infection, by simulating infection and sampling in pigsties. We found that impartial sampling, which involves selection of pigs to be sampled from the four corners and the center of the pigsty, and random sampling showed comparable probabilities of detection. Impartial sampling involves sample collection without pig identification and random selection. Owing to its simplicity, impartial sampling is labor-efficient and thus a possible substitute for random sampling. In a group-housing pigsty, testing five pigs from five pens showed a higher detection probability than testing five pigs from one pen. These results suggest preferable surveillance methods for conducting emergency surveillance of infectious diseases.

Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241177 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 41177&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0241177

DOI: 10.1371/journal.pone.0241177

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().