Abstract
Early detection of infectious diseases can substantially reduce the health and economic impacts on livestock production. Here we describe a system for monitoring animal activity based on video and data processing techniques, in order to detect slowdown and weakening due to infection with African swine fever (ASF), one of the most significant threats to the pig industry. The system classifies and quantifies motion-based animal behaviour and daily activity in video sequences, allowing automated and non-intrusive surveillance in real-time. The aim of this system is to evaluate significant changes in animals’ motion after being experimentally infected with ASF virus. Indeed, pig mobility declined progressively and fell significantly below pre-infection levels starting at four days after infection at a confidence level of 95%. Furthermore, daily motion decreased in infected animals by approximately 10% before the detection of the disease by clinical signs. These results show the promise of video processing techniques for real-time early detection of livestock infectious diseases.
Highlights
Late detection of emergency diseases promotes disease spread and increases the risk of epidemic, and it may cause significant economic losses in affected countries [1], as demonstrated by the last epidemics of foot-and-mouth disease [2, 3], classical swine fever [4, 5] or the current epidemic of African swine fever (ASF) [6]
We aim to demonstrate that animals infected with ASF virus show a progressive deceleration in performing daily activities caused by muscle weakness from early stages of infection, which should translate into reduced overall motion, which quantitative automatic video processing should be able to detect [9]
Video processing of livestock offers the possibility of non-intrusive animal monitoring in real time
Summary
Late detection of emergency diseases promotes disease spread and increases the risk of epidemic, and it may cause significant economic losses in affected countries [1], as demonstrated by the last epidemics of foot-and-mouth disease [2, 3], classical swine fever [4, 5] or the current epidemic of African swine fever (ASF) [6]. Classical strategies to detect diseases on the farm include active and passive surveillance as well as sentinel surveillance, which focuses on farms at higher risk of incursion [7]. The effectiveness of passive surveillance relies on the ability to detect disease based on the observation of clinical signs. This can pose a severe obstacle to early detection, if death is the most visible clinical sign as is the case with foot-and-mouth disease, classical swine fever or ASF. Farms may apply active surveillance approaches to gain information about disease prevalence on the farm under a sampling protocol based on appropriate.
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