Spatio-temporal analysis of the progression of Aujeszky’s disease virus infection in wild boar of Saxony-Anhalt, Germany

Abstract

Abstract. Aujeszky's disease (AD, pseudorabies) is a notifiable disease caused by Suid herpesvirus 1 (SuHV-1), also named pseudorabies virus (PrV). The study aimed at determining the occurrence and spatio-temporal trend of specific antibodies to AD virus (ADV) among wild boar of Saxony-Anhalt, a landlocked federal state situated in the western part of eastern Germany. To this end, a total of 7,209 blood samples were collected and tested from 2000 to 2011. An average seroprevalence of 6.8% was found for the entire observation period, whilst seroprevalence increased significantly between time periods January 2000-March 2009 (n = 3,605; prevalence = 4.5%) and March 2009-December 2011 (n = 3,604; prevalence = 9.1%). Spatial analysis revealed an extremely heterogenous distribution of seropositive samples with cluster formation [relative risk (RR) up to 41, P = 0.001] in the east. A comparison of spatial clusters between the aforementioned time intervals showed an expansion of the disease in the north to north-westerly direction. A test for spatial shift involving the entire territory of Saxony-Anhalt detected a spread of ADV infection in the latter direction (P = 0.079) at the average speed of 3.3 km/year. Detailed study of the distribution and spread of the disease among wild boar, including assessment of the speed of spatial spread, has not been done in the region before. Saxony-Anhalt has been officially recognised as being free of AD in domestic pigs since 1996. Despite increasing ADV seroprevalences in the wild boar population of Saxony-Anhalt and other federal states in the east of Germany, no spill-over to domestic pigs have been reported. The currently implemented monitoring will be continued in conjunction with surveillance of classical swine fever (CSF) to further trace ADV infections in the wild boar population of Saxony-Anhalt.

Monitoring of Pseudorabies in Wild Boar of Germany—A Spatiotemporal Analysis

“…While previous approaches to assess PRV seroprevalence in wild boar populations of Germany mainly focused on East Germany [22,[24][25][26] and only occasionally targeted regions in other parts of the country [21,[27][28][29], this study represents the first nationwide monitoring of PRV infection in wild boars. Unfortunately, for reasons mentioned above, the monitoring was spatially and temporally incomplete (Table 1, Figure 2).…”

“…Even for the Eastern parts of Germany, it has been suggested that the increase in PRV seroprevalence over a 24 year observation period (1985-2008) is a consequence of strong disease dynamics, which may have led to a westward spread at an average speed of 3.3 km/year [25,26,60]. However, phylogenetic evidence suggests at least two different PRV variants are circulating in wild boar populations in Germany.…”

“…The main objectives of this study were to: (i) gain an overview of the dimension of the spread of PRV infections in wild boar at a national level, (ii) estimate PRV seroprevalences at a As in many other European countries, PRV is also present in the wild boar population in Germany, where infections in wild boar are known to be endemic, particularly in the eastern parts of the country (the former German Democratic Republic), a region that comprises about one-third of Germany's total area [21][22][23][24]. Continuous serological monitoring of wild boar in this particular area over more than two decades has provided valuable insights into the evolution, spread and dynamics of wild boar-mediated PRV infections [25,26]. PRV seroprevalences were estimated at 16.5% on average for the whole region but reached up to 45% in endemic hot spots [25].…”

A picture of trends in Aujeszky’s disease virus exposure in wild boar in the Swiss and European contexts

“…The highest seroprevalences have been documented in Mediterranean countries including Spain (up to 100 %) [ 1 , 17 , 34 – 39 ], Italy (up to 51 %) [ 40 , 41 ] and Croatia (up to 57 %) [ 42 , 43 ], as well as in Romania (55 %) [ 44 ]; followed by central and eastern European countries such as Slovenia (31 %) [ 45 , 46 ], Austria (38 %) [ 47 ], Czech Republic (30 %) [ 48 ] and northeastern Germany (up to 29 %) [ 19 , 49 ]. In contrast, there is an area with low to moderate ADV seroprevalences in the centre and north of Europe: Switzerland (<4 %) [ 31 , 50 ], the Netherlands (0 %) [ 51 – 53 ], Sweden (0 %) [ 33 , 54 – 58 ], parts of France [ 13 , 59 – 61 ] and of Germany [ 19 , 49 , 62 – 64 ]. Within this area of low seroprevalences, multiple regions with higher seroprevalences exist: Although the overall seroprevalence of continental France lies at 6 %, several provinces in the centre (Le Loir-et-Cher, le Loiret), in the northwest (l’Ille-et-Villaine), in the Mediterranean area (Corse) and the north-east of France (les Ardennes, la Meuthe-et-Moselle, la Meuse) reach levels between 21 and 54 % [ 60 ].…”

Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems

“…Identification of significant disease clusters can also advance our understanding of a disease in several ways including suggesting potential risk factors for further investigation either directly (Calistri et al, 2013;French et al, 2005;Sinkala et al, 2014;Kelen et al, 2012;Nogareda et al, 2013;Poljak et al, 2007;Le et al, 2012;Vigre et al, 2005;Ward and Carpenter, 2000), or indirectly when analysis of model residuals indicates the modelled predictors do not explain fully the spatial heterogeneity in disease distribution (Méroc et al, 2014;Borba et al, 2013), or by defining the scale of disease clustering (French et al, 2005;Le et al, 2012;French et al, 1999;Wilesmith et al, 2003;Picado et al, 2007;Picado et al, 2011;Porphyre et al, 2007;Sanchez et al, 2005;Minh et al, 2010;Xu et al, 2012;Métras et al, 2012;Abatih and Ersbøll, 2009) and thereby indicate likely transmission mechanisms involved in disease spread (Sinkala et al, 2014;Ward et al, 2013;Loobuyck et al, 2009;Ohlson et al, 2014;Rosendal et al, 2014;Poljak et al, 2010). Cluster detection can also be used identify areas where vectors and hosts coincide resulting in potentially increased risk of disease transmission (Shaman, 2007;Hennebelle et al, 2013;Swirski et al, 2007), highlight possible regional differences in disease transmission (Kelen et al, 2012), or track the direction and geographical extent of disease spread (Wilesmith et al, 2003;Denzin et al, 2013;…”