Modelling African swine fever virus spread in pigs using time‐respective network data: Scientific support for decision makers. Issue 5 (18th April 2022)

• Record Type: Journal Article
• Authors: Andraud, Mathieu; Hammami, Pachka; H. Hayes, Brandon; A. Galvis, Jason; Vergne, Timothée; Machado, Gustavo; Rose, Nicolas
• Is Part Of: Transboundary and emerging diseases. Volume 69:Issue 5(2022)
• Page Start: e2132
• Page End: e2144
• Publication Date: 2022-04-18
• Subjects: control -- epidemiology -- infectious diseases -- surveillance -- transmission model; Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1111/tbed.14550
• Languages: English
• ISSNs: 1865-1674

Abstract

Abstract: African swine fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic timescale. This approach allows for a rapid update of input data without any pre‐treatment, which could be important in terms of responsiveness, should an introduction occur.