Spread of porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds: Part I. Exploratory spatial analysis

Abstract

BackgroundThe systemic form of porcine circovirus associated disease (PCVAD), also known as postweaning multisystemic wasting syndrome (PMWS) was initially detected in the early 1990s. Starting in 2004, the Canadian swine industry experienced considerable losses due to PCVAD, concurrent with a shift in genotype of porcine circovirus type 2 (PCV2). Objectives of the current study were to explore spatial characteristics of self-reported PCVAD distribution in Ontario between 2004 and 2008, and to investigate the existence and nature of local spread.ResultsThe study included 278 swine herds from a large disease-monitoring project that included porcine reproductive and respiratory syndrome (PRRS) virus-positive herds identified by the diagnostic laboratory, and PRRS virus-negative herds directly from the target population. Herds were included if they had growing pigs present on-site and available geographical coordinates for the sampling site. Furthermore, herds were defined as PCVAD-positive if a producer reported an outbreak of circovirus associated disease, or as PCVAD-negative if no outbreak was noted. Spatial trend was investigated using generalized additive models and time to PCVAD outbreak in a herd using Cox's proportional hazard model; spatial and spatio-temporal clustering was explored using K-functions; and location of most likely spatial and spatio-temporal clusters was investigated using scan statistics. Over the study period, the risk of reporting a PCVAD-positive herd tended to be higher in the eastern part of the province after adjustment for herd PRRS status (P = 0.05). This was partly confirmed for spread (Partial P < 0.01). Local spread also appeared to exist, as suggested by the tentative (P = 0.06) existence of spatio-temporal clustering of PCVAD and detection of a spatio-temporal cluster (P = 0.04).ConclusionsIn Ontario, PCVAD has shown a general trend, spreading from east-to-west. We interpret the existence of spatio-temporal clustering as evidence of spatio-temporal aggregation of PCVAD-positive cases above expectations and, together with the existence of spatio-temporal and spatial clusters, as suggestive of apparent local spread of PCVAD. Clustering was detected at small spatial and temporal scales. Other patterns of spread could not be detected; however, survival rates in discrete Ontario zones, as well as a lack of a clear spatial pattern in the most likely spatio-temporal clusters, suggest other between-herd transmission mechanisms.

Dynamics of Virus Distribution in a Defined Swine Production Network Using Enteric Viruses as Molecular Markers

“…This study supports and extends recent studies regarding disease spread modeling in Canadian swine networks (2,34,47,48) by reporting on real-time molecular detection of enteric viruses used as indicators in a dynamic and complex production network. In addition, results reported here underline the great potential of genetically heterogeneous enteric viruses as useful markers in epidemiological studies.…”

A scoping review of spatial cluster analysis techniques for point-event data

“…The primary use of K-function analysis was exploring the presence and scale of spatial clustering of the selected exposure variables (Austin et al, 2005;Hillier et al, 2009;Day and Pearce, 2011). The K-function was also used to assess the spatial structure of a distribution before conducting local analyses of spatial clustering (Han et al, 2004;Broman et al, 2006;Wheeler, 2007;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010). Knowing the scale and structure of the spatial dependency among data helps the user confirm whether local analyses are required as well as provide an approximation of spatial weight specifications.…”

“…Over half of papers in this review applied the spatial scan statistic to examine the spatial patterns of address location data (Andrade et al, 2004;Brooker et al, 2004;Han et al, 2004;Polack et al, 2005;Bautista et al, 2006;Chaix et al, 2006;Ernst et al, 2006;Pollack et al, 2006;Sarkar et al, 2007;Wheeler, 2007;Pasma, 2008;Warden, 2008;Huang et al, 2009;Meliker et al, 2009;Tanser et al, 2009;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010;Westercamp et al, 2010;Winskill et al, 2011). Of the reviewed articles, 83% applied a Bernoulli model spatial scan statistic to case-control data; two of those articles also used other models in SatScan that can be applied to address location data, the discrete normal continuous model ) and the discrete poisson continuous model (Ngowi et al, 2010), ordinal model (Westercamp et al, 2010) and the multinomial model (Westercamp et al, 2010).…”

“…Of the 29 papers reviewed, five used GAMs. All five papers utilized the GAM approach for exploratory purposes (SiqueiraJunior et al, 2008;Vieira et al, 2008Vieira et al, , 2009Vieira et al, , 2010Poljak et al, 2010). A primary goal in epidemiology is the explanation of processes generating spatial and temporal patterns of disease and disease risks.…”

“…GAMs can be applied in a space only, spatial-temporal, or time only frameworks (Vieira et al, 2009;Poljak et al, 2010) and have been cited to be particularly useful for analysing longitudinal data that incorporate residential history patterns (Vieira et al, 2010). The spatial output of the variety of analyses are communicated through a smoothed risk surface, aiding visual recognition of patterns, a technique often used as an exploratory spatial data analysis (Poljak et al, 2010). The prime advantage of GAMs is the ability to control for the underlying population distribution from spatial control locations -similar to the KIF -as well as covariates.…”