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A Case Study of the Application of Artificial Intelligence to Aid in the Clinical Detection of Porcine Reproductive and Respiratory Syndrome Virus in Sow Farms

by Benjamin W. Blair, Sasidhar Malladi, James F. Lowe
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 9
Year of Publication: 2022
Authors: Benjamin W. Blair, Sasidhar Malladi, James F. Lowe
10.5120/ijca2022922061

Benjamin W. Blair, Sasidhar Malladi, James F. Lowe . A Case Study of the Application of Artificial Intelligence to Aid in the Clinical Detection of Porcine Reproductive and Respiratory Syndrome Virus in Sow Farms. International Journal of Computer Applications. 184, 9 ( Apr 2022), 13-20. DOI=10.5120/ijca2022922061

@article{ 10.5120/ijca2022922061,
author = { Benjamin W. Blair, Sasidhar Malladi, James F. Lowe },
title = { A Case Study of the Application of Artificial Intelligence to Aid in the Clinical Detection of Porcine Reproductive and Respiratory Syndrome Virus in Sow Farms },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2022 },
volume = { 184 },
number = { 9 },
month = { Apr },
year = { 2022 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number9/32356-2022922061/ },
doi = { 10.5120/ijca2022922061 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:02.206299+05:30
%A Benjamin W. Blair
%A Sasidhar Malladi
%A James F. Lowe
%T A Case Study of the Application of Artificial Intelligence to Aid in the Clinical Detection of Porcine Reproductive and Respiratory Syndrome Virus in Sow Farms
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 9
%P 13-20
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The introduction of a pathogen into a livestock population is the cause devastating losses in swine farms. The early detection of disease plays an essential role in limiting the effects of a pathogen introduction on a population. While the use of routine lab-based molecular testing for antigens can aid in rapid pathogen detection, the number of samples statistically required, and the associated expense make its frequent use unrealistic. For these reasons, researchers continue to develop additional tools to aid in disease detection. This project proposes a predictive tool that overcomes the limitations of inherent biological variation by utilizing current machine learning advancements to detect disease quickly and accurately within a population. The early identification of a pathogen on a sow farm facilitates timely management decisions to slow pathogen transmission and reduce the severity of disease. Producers’ decisions encompass a broad range of tactics to limit spread through the feed, people, supply, or animal movements. Prompt implementation of these tactics is essential for minimizing both the individual producer's and the industry's short- and long-term financial losses. Described is a tool that facilities sensitive syndromic surveillance for sow farms by applying machine learning to historical individual sow production records to predict of the outcome of an individual breeding event. The tool predicts which sow breeding events will yield piglets (farrow) and subsequently monitors the outcome of the breeding event. If more services result in failure than expected, as defined by the model's error, the model signals a disruption (presence of disease). To compare the sensitivity of the tool to the established Statistical process control approach (SPC), retrospective data from two sow farms that experienced a Porcine reproductive and respiratory syndrome virus (PRRSv) introduction were assessed. While both the machine learning based tool and SPC detected PRRSv introduction on each farm, the average detection was 1 and 3 weeks before the farm reported a disease event using the novel machine learning-based method and 2 weeks after and 1 week before using SPC. In addition to identifying the PRRSv introduction, the machine learning approach identified production disruption resulting from changes to the electronic sow feeding system on the farm. SPC failed to identify this disruption. These two test cases demonstrate that the novel machine learning-based method maybe more sensitive for the surveillance of swine farms for pathogen and non-pathogen related disruptions to average production compared to previously described SPC based approach. The machine learning based technique is broadly applicable to economically important diseases, and most importantly can serve as an early alert for novel pathogens where industry level monitoring is not conducted routinely.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine learning surveillance swine disease SPC epidemiology

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