Fig 1.
Farm structure, facilities and populations considered.
Farms can be composed of one to four sectors depending on their type: gestation, farrowing, post-weaning and finishing sectors (coloured squares). Each sector is divided into rooms (dashed lines), that are composed of pens (white squares). Two populations are considered: breeding sows (red triangles) and growing pigs (blue dots).
Table 1.
Types of sectors, animal populations and events per farm depending on the farm type.
Farms are composed of one to four sectors, depending on their type: nucleus (SEL), multiplication (MU), farrow-to-finish (FF), farrowing (FA), farrowing post-weaning (FPW), post-weaning (PW), post-weaning finishing (PWF) and finishing (FI) farms. They can rear one or two populations (breeding sows, growing pigs). Six types of events can occur depending on the farm type: movement of sows from gestation to farrowing sector (ges-fa); piglet birth (birth); movement of sows from farrowing back to gestation sector (fa-ges); movement of piglets from farrowing to post-weaning sector (fa-pw); movement of growing pigs from post-weaning to finishing sector (pw-fi); movement of growing pigs leaving the finishing sector (fi).
Table 2.
Parameters governing the population dynamics model in a 7-batch rearing system.
FA: farrowing farms, FPW: farrowing post-weaning farms, SEL: nucleus farms, MU: multiplication farms, FF: farrow-to-finish farms.
Fig 2.
Selection process of the movements’ destinations.
Each time animals have to be shipped from a sector, as defined by the production cycle, the type of event (i.e. internal versus external) is determined according to the probability pExt that is the probability that animals are shipped externally, as defined by the population data. In cases of no free pens found internally (resp. externally), external (resp. internal) movement is considered. If all pens (internally and in contact farms) are full, animals are sent to slaughterhouse. If animals are shipped externally, the destination site is sampled in the contact neighbours of the farm of origin, the probability pCont of a destination farm to be sampled being defined in the population data.
Fig 3.
Largest community in the pig movement network in France (2012–2014), derived from Salines et al. [35].
Using Infomap algorithm, a large community including 3,017 farms was identified in the French pig movement network (data from 2012 to 2014). Farm and movement data from this community was used as input population data in the present model. The size of the dots is proportional to the total degree of the holding, the colours are related to the farm type. FI: finishing farm, FF: farrow-to-finish farm, FPW: farrowing post-weaning farm, PWF: post-weaning finishing farm, MU: multiplication farm, FA: farrowing farm, SEL: nucleus farm, PW: post-weaning farm.
Fig 4.
HEV infection process as represented with a MSEIRS model.
The epidemiological model has been built as a MSEIR–Maternally Immune (M), Susceptible (S), Exposed (E), Infectious (I) and Recovered (R)–model including an environmental compartment. MDAs: maternally-derived antibodies.
Table 3.
Transition rates for each health state transition as illustrated in Fig 4.
λ is the global force of infection as described in Eqs (1) and (3), ρ is the latency rate for exposed animals E, γ is the recovery rate for infectious animals I, σ and μ denote the maternal and active immunity waning respectively.
Table 4.
Epidemiological parameters governing the HEV infection dynamics in cases of IMV-free or IMV-positive farms.
IMV: immunomodulating virus.
Table 5.
Description of the different scenarios (S) of the HEV between-herd model.
IMV: immunomodulating virus, SEL: nucleus farm, MU: multiplication farm, FF: farrow-to-finish farm, FA: farrowing farm.
Fig 5.
Distribution of the number of HEV positive farms depending on the scenario.
S: scenario; FF: farrow-to-finish pig farm.
Table 6.
Effect of the index farm and of the IMV situation in the community on the farm-level prevalence over the study period.
Summary statistics obtained thanks to a multivariate logistic regression.
Table 7.
Effect of population and farm features on the farms’ time to HEV infection.
Summary statistics obtained thanks to a cox-proportional hazard model with the simulation being included as a frailty effect.
Table 8.
Effect of the type of the index farm and of the IMV situation in the community on the proportion of HEV-positive pigs sent to the slaughterhouse.
Summary statistics obtained thanks to a generalised estimating equation (GEE) logistic regression model with the simulation being included as a repeated statement.