A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network

Abstract

Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible co-infection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix. Between-herd transmission was modelled by coupling within-herd and network dynamics using the SimInf package. Different introduction scenarios were tested as well as a decrease in the prevalence of IMV-infected farms. After introduction of a single infected gilt, the model showed that the transmission pathway as well as the prevalence of HEV-infected pigs at slaughter age were affected by the type of the index farm, the health status of the population and the type of the infected farms. These outcomes could help design HEV control strategies at a territorial scale based on the assessment of the farms’ and network’s risk.

The between‐farm transmission dynamics of porcine epidemic diarrhoea virus: A short‐term forecast modelling comparison and the effectiveness of control strategies

“…A lot of effort has been made to forecast infectious disease in humans, but uncertainty about behavioural and public health interventions can preclude reliable predictions (Desai et al., 2019; Petropoulos & Makridakis, 2020:19). While original models have been developed to assess the contribution of different transmission mechanics to pathogen spread among swine populations (Bastard et al., 2020; Galvis et al., 2020; Salines et al., 2020; VanderWaal et al., 2018) and fitting models to available prevalences (Bastard et al., 2020), but forecasting the future trajectory of outbreaks is rarely done (Andraud & Rose, 2020; Galvis et al., 2020), mainly because of the unavailability of empirical epidemiological data. However, models have indeed been used to inform decision‐makers during planning contingency and surveillance strategies (Ezanno et al., 2020; Salines et al., 2020; Sørensen et al., 2018).…”

“…While original models have been developed to assess the contribution of different transmission mechanics to pathogen spread among swine populations (Bastard et al., 2020; Galvis et al., 2020; Salines et al., 2020; VanderWaal et al., 2018) and fitting models to available prevalences (Bastard et al., 2020), but forecasting the future trajectory of outbreaks is rarely done (Andraud & Rose, 2020; Galvis et al., 2020), mainly because of the unavailability of empirical epidemiological data. However, models have indeed been used to inform decision‐makers during planning contingency and surveillance strategies (Ezanno et al., 2020; Salines et al., 2020; Sørensen et al., 2018). Therefore, combining current knowledge of PEDV with mechanistic transmission models has the potential to fill important gaps in our understanding of PEDV transmission dynamics, as well as providing tools for prioritizing the surveillance and control of PEDV.…”

Modeling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus

“…Direct contact among farms through pig transportation together with indirect contacts often defined as local spread or local transmission, (e.g. airborne, feed deliveries, shared equipment) have been identified as key routes of swine bacteria and virus dispersal (Velasova et al, 2012; Lee et al, 2017; VanderWaal et al, 2018; Silva et al, 2019; Bastard et al, 2020; Salines et al, 2020). Previous experimental and observational studies have reported frequent PRRSV reinfection events on stable breeding farms (i.e., farrow-to-wean), in this paper we will referred to re-break events (Pileri and Mateu, 2016).…”

“…Even though there is no clear consensus in the definition of what constitutes a re-break, the rebound of PRRSV could be related to the continued viral shedding within groups of pigs and/or reduced cleaning and disinfection procedures among other unknown factors and dynamics (Bierk et al, 2001; Arruda et al, 2016). Despite the initial findings, the quantification of PRRSV transmission among multiple farm types (e.g., from finisher farms to farrow-to-wean), remains a major gap in PRRSV epidemiology (Jara et al, 2020; Salines et al, 2020).…”

Modelling infectious viral diseases in swine populations: a state of the art

“…A homogeneous mixing assumption is generally used to represent within-herd infection dynamics coupled with between-herd transmission module [43,56,98], but Kinsley et al showed that the incorporation of accurate population dynamics in herds might shed new light on the actual infectious process [99]. A recent study on HEV spread integrated the compartment structure in pig herds in the SimInf framework [100], a modelling framework originally developed to study VTEC-O157 spread among cattle herds in Sweden [101]. Although more complex; this model accounted for realistic representation of the population demographics, together with the commercial network between production sites based on batch-rearing system.…”