Accordingly, a probiotic approach to the control of drinking water borne opportunistic pathogens has previously been suggested. Additionally, we know that corrosion of other critical infrastructure systems, e.g. sewers, is driven by their micro-biome.Important factors in the building micro-biome are geographic location, occupancy, ventilation rate, and ventilation type , but there are many uncertainties within these factors. For example, while ventilation has been suggested to be a primary driver of the built environment microbial community as a source of microorganisms from outdoor air, the precise influence of ventilation type and operation warrants further investigation. Similarly, the roles of temperature, relative humidity, and light intensity in structuring the micro-biome remain unclear. Further, we know much more about the impact of these factors on the relative abundances of particular taxa than we do about absolute abundances of individual species, their viability, and their function in indoor settings. It would be powerful to be able to predict the micro-biome of indoor spaces and their community dynamics based on knowledge of building factors.With 146 million pigs and a yearly production of about 22 million tons of carcass weight, the European Union is the world’s top exporter and the second biggest producer of pig meat after China . However, several transboundary animal diseases , such as African swine fever , classical swine fever , or foot-and-mouth disease , are of permanent risk of introduction or reintroduction in the EU swine industry . Given the devastating impact outbreaks of such diseases can have on farmers, society, and EU countries economy, the European Commission strengthened the need of preparedness at both national and international levels to mitigate diseases risks and impacts . Epidemic models are increasingly used to evaluate and inform disease surveillance and control policies . As animal trade play a key role in the spread and control of most of TADs ,greenhouse bench tops it is essential to include trade movement patterns to more realistically and accurately simulate the spatiotemporal spread of diseases and the effectiveness of control measures .
Since Regulation no. 1760/2000 of the European parliament, data on pig trade movements are registered at a farm level and daily scale in EU member countries. The full trade networks can be integrated in epidemic models to produce more realistic disease spread simulations [e.g., Ref. ]. However, considering the amount of data available, modeling transmission through full networks is computationally challenging and time-consuming, which would limit the usefulness of such models in a crisis period. Different methodologies can be used to simplify and incorporate the major properties of pig trade patterns in epidemic models. Previous studies mostly used statistics on shipments rates, shipment distances, and mixing patterns between production types . Others included statistics on network topology , as it has been shown that disease spread is sensitive to the topological structure of the contact network . These statistics come from country specific data, expert opinions, or from countries with similar production systems . However, it is not clear how the parameters from one country can be translated to other areas , and few data are available for some specific production systems, such as outdoor or small scale production systems . Moreover, different production systems might coexist within a country, but their specific trade patterns might be hidden when computing statistics at country level. Community detection algorithms have been used to detect groups of premises that tend to trade together . They could be useful to identify different production systems within a country and better characterize their specific trade organization. The objective of this paper was to fill part of those knowledge gaps by unraveling the functional and spatial organization of pig trade in the EU. Our aim was particularly to characterize and compare the trade structure and patterns in different pig production systems, including small-scale and extensive systems, for which scarce information is available so far.
Results would be useful to better inform surveillance and control strategies as well as to more realistically parameterize disease spread models, particularly for TADs and other swine diseases with high economic impact, such as porcine respiratory and reproductive syndrome .Four countries were selected to represent the diversity of European pig production systems: Bulgaria, France, Italy, and Spain. Spain and France are the second and third producers of pig meat in the EU, with intensive production systems, i.e., large-scale high density indoor herds, concentrated mostly in Cataluña, Murcia, and Bretagne . Italy is the seventh producer in the EU with intensive farming concentrated in the northern regions but also with high number of semi-intensive, medium, and small farms . In Bulgaria, such as other Eastern European countries, pigs are mostly reared by small producers , mostly, for self-consumption . Beside these systems, several regions have preserved traditional extensive production systems involving local breeds that are reared outdoor for the production of high quality cured meat. Such systems are observed in south-central Spain, in south-west and central France, in south-central Italy, in the French and Italian Mediterranean islands of Corsica and Sardinia, and in the Eastern mountains in Bulgaria .Data on pig movements and premises characteristics were obtained from national databases, through Bulgarian Food Safety Agency in Bulgaria, the professional database of swine in France, the Istituto Zooprofilattico Sperimentale dell’Umbria e Marche in Italy, and the Ministry of Agriculture, Food and Environment in Spain, under the appropriate confidential data transfer agreements. Registration of pig movements is mandatory in these countries since, at least, 2009. The year 2011, which was common in all databases, was retained for the analysis. Because of the dead-end characteristics of slaughterhouses, these premises were excluded from the analysis. The premises characteristics available were the type of production, the premise size, the type of housing system , the geographical coordinates, as well as the pig company number . In Bulgaria, pig farms were classified as East Balkan pigs , SP , Type B farms , Type A farms , or industrial farms .
For France, Italy, and Spain, pig premises were categorized into seven distinct types: multipliers , farrowing farms , farrow-to finishing farms , finishing farms , SP, trade operators , and unknown premise type . FA included farms which produce piglets until 3 or 25 kg. FI included farms which buy piglets and produce either 25 kg piglets or fattening pigs. For Italy, farrowers and farrow-to-finishers could not be distinguished in the database and were thus both typed as FA. SP were defined as those who produce pigs for self-consumption in Spain, those who have no more than four fattening pigs and produce pigs for self-consumption in Italy , and farms with no more than four pigs in France.Trade operators included traders, collection centers, markets, fairs, and stop points. For those farms with no available coordinates, the centroid location of the smallest geographical administrative unit available was used. The main characteristics of the study area and study pig industries are presented in Table 1. Information on trade movements for all countries included the date of the movement, the unique identifier of the source and destination premises, and the number of pigs moved. For each country,commercial drying rack directed and weighted yearly networks were built, the nodes being all pig premises of the study areas, even those that were not trading pigs during the study period. Movement data were aggregated over the study period and a direct link was drawn whenever a shipment of pigs occurred between the corresponding premises. Two weights wij A and wij B were attributed to the link according to the number of pig batches and the number of pigs moved from premise i to premise j during the study period, respectively. The premises were considered as “active” if they moved pigs during the study period.Descriptive statistics of the pig shipments are presented in Table 3. Shipment rates were generally quite low with a median <1–6 in going and 3–8 outgoing shipments per active premise per year . Heterogeneity was observed between premises and between types of premises, with particularly high rates of incoming shipments for trade operators in France . Median shipment distances varied from 3 km to 44 km . The premise type mostly sending pigs over long-range distances were industrial and type A farms in Bulgaria, multipliers in France and Spain, and trade operators in Italy . Median shipment sizes varied from 4 to 220 pigs . Shipment sizes tended to be higher when the pigs were sent to industrial farms in Bulgaria and to finishing farms in France, Italy, and Spain . Pig batches sent to SP tended to be of small size and to come from local source . Different mixing patterns by premise types were observed according to the country . In Bulgaria, industrial and EPB farms tended to trade with premises of the same type, whereas Type A farms tended to be intermediate between Type B and industrial farms. Multipliers tended to send pigs to multipliers, farrowing and farrow-to-finishing farms in France and Spain, whereas they were more likely to send pigs to multipliers only in Italy. Trade operators tended to be intermediate between farms and other trade operators in France and Spain, whereas they also tended to send pigs to multipliers and producers in Italy.Trade networks were divided into 174, 842, 3,070, and 4,362 communities in Bulgaria, France, Italy, and Spain, respectively. The communities were more isolated, i.e., had fewer pig batches moved to or from premises of other communities, in Bulgaria than in the other countries .
Fourteen, 15, 15, and 9 large communities were identified according to the distribution of community sizes in Bulgaria, France, Italy, and Spain, respectively. They included 37.7% , 51.6% , 15.6% , and 13.7% of active premises. Based on the distribution of the production types and housing system , three types of production systems could be defined: type 1 – intensive: more than 50% of premises were commercial pig farms and <10% raised pigs outdoor; type 2 – commercial outdoor: more than 50% of premises were commercial pig farms and more than 10% raised pigs outdoor; and type 3 – small-scale: more than 50% of premises were small-scale pig farms, raising pigs indoor or outdoor. Only two of the largest communities were of intensive type in Bulgaria, the other being of small-scale type. In France and Spain, most of the largest communities were intensive, except five communities that were of commercial outdoor type. They were located in southwestern, center, and eastern regions of France and in Extremadura and south of Castille y Leon in Spain. In Italy, only three of the largest communities were of intensive type and were located in Lombardia and Piemonte. The others were of small-scale type and were located in center and southern regions of Italy. All communities formed spatial clusters, which tended to cover quite large areas and to overlap when the production system was intensive, but were highly spatially clustered when it was small-scale . All communities were scale-free with average power law scaling parameters comprised between 2.1 and 7.5. All communities of intensive type that included trade operators exhibited small-world properties . The other communities with small-world properties were two communities of SP that included trade operators in Italy . Communities of small-scale type exhibited a star-topology type, reflected by a null clustering coefficient and an average path length of 1 . These communities usually consisted of a commercial farm that sent pigs to SP .This study provides a better understanding of the pig trade structure and characteristics in the EU under diverse production systems, including intensive, commercial outdoor, and small-scale. We also provide valuable proxies for pig movement patterns at country and community levels that can be used to better parameterize more realistic epidemic models under diverse epidemiological scenarios. Results also improve our understanding of trade drivers by highlighting similarities and differences in the functional and spatial organization of pig trade between countries and between production systems. One of the challenges of this study was to identify and describe European pig production systems, which may have different trading patterns and thus different behaviors regarding infectious diseases but can coexist within a country.