Understanding African Swine Fever infection dynamics in Sardinia using a spatially explicit transmission model in domestic pig farms.

African swine fever virus (ASFV) has been endemic in Sardinia since 1978, resulting in severe losses for local pig producers and creating important problems for the island's veterinary authorities. This study used a spatially explicit stochastic transmission model followed by two regression models to investigate the dynamics of ASFV spread amongst domestic pig farms, to identify geographic areas at highest risk and determine the role of different susceptible pig populations (registered domestic pigs, non-registered domestic pigs [brado] and wild boar) in ASF occurrence. We simulated transmission within and between farms using an adapted version of the previously described model known as Be-FAST. Results from the model revealed a generally low diffusion of ASF in Sardinia, with only 24% of the simulations resulting in disease spread, and for each simulated outbreak on average only four farms and 66 pigs were affected. Overall, local spread (indirect transmission between farms within a 2 km radius through fomites) was the most common route of transmission, being responsible for 98.6% of secondary cases. The risk of ASF occurrence for each domestic pig farm was estimated from the spread model results and integrated in two regression models together with available data for brado and wild boar populations. There was a significant association between the density of all three populations (domestic pigs, brado, and wild boar) and ASF occurrence in Sardinia. The most significant risk factors were the high densities of brado (OR = 2.2) and wild boar (OR = 2.1). The results of both analyses demonstrated that ASF epidemiology and infection dynamics in Sardinia create a complex and multifactorial disease situation, where all susceptible populations play an important role. To stop ASF transmission in Sardinia, three main factors (improving biosecurity on domestic pig farms, eliminating brado practices and better management of wild boars) need to be addressed.

Why is African swine fever still present in Sardinia?

African swine fever (ASF) is an infectious disease of swine that has been present in Sardinia since 1978. Soon after introduction of the disease, several control and eradication programmes were established with limited success. Some researchers attributed the persistence of the disease in central and eastern areas to certain socio-economic factors, the existence of some local and traditional farming practices (i.e., unregistered free-ranging pigs known as brado animals) and the high density of wild boar in the region. In the past, scarcity of swine data in Sardinia complicated the evaluation and study of ASF on the island. More complete, accurate and reliable information on pig farms has become available as a result of the most recent eradication programmes. Here, we perform statistical modelling based on these data and the known distribution of domestic pig and wild boar to identify the main risk factors that have caused ASF persistence in Sardinia. Our results categorized, identified and quantified nine significant risk factors, six of which have not been previously described. The most significant factors were the number of medium-sized farms, the presence of brado animals and the combination of estimated wild boar density and mean altitude above sea level. Based on these factors, we identified regions in eastern and central Sardinia to be at greatest risk of ASF persistence; these regions are also where the disease has traditionally been endemic. Based on these risk factors, we propose specific control measures aimed at mitigating such risks and eradicating ASF from the island.

Early Detection of Infection in Pigs through an Online Monitoring System.

Late detection of emergency diseases causes significant economic losses for pig producers and governments. As the first signs of animal infection are usually fever and reduced motion that lead to reduced consumption of water and feed, we developed a novel smart system to monitor body temperature and motion in real time, facilitating the early detection of infectious diseases. In this study, carried out within the framework of the European Union research project Rapidia Field, we tested the smart system on 10 pigs experimentally infected with two doses of an attenuated strain of African swine fever. Biosensors and an accelerometer embedded in an eartag captured data before and after infection, and video cameras were used to monitor the animals 24 h per day. The results showed that in 8 of 9 cases, the monitoring system detected infection onset as an increase in body temperature and decrease in movement before or simultaneously with fever detection based on rectal temperature measurement, observation of clinical signs, the decrease in water consumption or positive qPCR detection of virus. In addition, this decrease in movement was reliably detected using automatic analysis of video images therefore providing an inexpensive alternative to direct motion measurement. The system can be set up to alert staff when high fever, reduced motion or both are detected in one or more animals. This system may be useful for monitoring sentinel herds in real time, considerably reducing the financial and logistical costs of periodic sampling and increasing the chances of early detection of infection.

Implementation and validation of an economic module in the Be-FAST model to predict costs generated by livestock disease epidemics: Application to classical swine fever epidemics in Spain.

Be-FAST is a computer program based on a time-spatial stochastic spread mathematical model for studying the transmission of infectious livestock diseases within and between farms. The present work describes a new module integrated into Be-FAST to model the economic consequences of the spreading of classical swine fever (CSF) and other infectious livestock diseases within and between farms. CSF is financially one of the most damaging diseases in the swine industry worldwide. Specifically in Spain, the economic costs in the two last CSF epidemics (1997 and 2001) reached jointly more than 108 million euros. The present analysis suggests that severe CSF epidemics are associated with significant economic costs, approximately 80% of which are related to animal culling. Direct costs associated with control measures are strongly associated with the number of infected farms, while indirect costs are more strongly associated with epidemic duration. The economic model has been validated with economic information around the last outbreaks in Spain. These results suggest that our economic module may be useful for analysing and predicting economic consequences of livestock disease epidemics.

A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: the example of FMD in Peru.

This study presents a multi-disciplinary decision-support tool, which integrates geo-statistics, social network analysis (SNA), spatial-stochastic spread model, economic analysis and mapping/visualization capabilities for the evaluation of the sanitary and socio-economic impact of livestock diseases under diverse epidemiologic scenarios. We illustrate the applicability of this tool using foot-and-mouth disease (FMD) in Peru as an example. The approach consisted on a flexible, multistep process that may be easily adapted based on data availability. The first module (mI) uses a geo-statistical approach for the estimation (if needed) of the distribution and abundance of susceptible population (in the example here, cattle, swine, sheep, goats, and camelids) at farm-level in the region or country of interest (Peru). The second module (mII) applies SNA for evaluating the farm-to-farm contact patterns and for exploring the structure and frequency of between-farm animal movements as a proxy for potential disease introduction or spread. The third module (mIII) integrates mI-II outputs into a spatial-stochastic model that simulates within- and between-farm FMD-transmission. The economic module (mIV) connects outputs from mI-III to provide an estimate of associated direct and indirect costs. A visualization module (mV) is also implemented to graph and map the outputs of module I-IV. After 1000 simulated epidemics, the mean (95% probability interval) number of outbreaks, infected animals, epidemic duration, and direct costs were 37 (1, 1164), 2152 (1, 13, 250), 63 days (0, 442), and US$ 1.2 million (1072, 9.5 million), respectively. Spread of disease was primarily local (<4.5km), but geolocation and type of index farm strongly influenced the extent and spatial patterns of an epidemic. The approach is intended to support decisions in the last phase of the FMD eradication program in Peru, in particular to inform and support the implementation of risk-based surveillance and livestock insurance systems that may help to prevent and control potential FMD virus incursions into Peru.