Computer Vision Applied to Detect Lethargy through Animal Motion Monitoring: A Trial on African Swine Fever in Wild Boar.

Early detection of infectious diseases is the most cost-effective strategy in disease surveillance for reducing the risk of outbreaks. Latest deep learning and computer vision improvements are powerful tools that potentially open up a new field of research in epidemiology and disease control. These techniques were used here to develop an algorithm aimed to track and compute animal motion in real time. This algorithm was used in experimental trials in order to assess African swine fever (ASF) infection course in Eurasian wild boar. Overall, the outcomes showed negative correlation between motion reduction and fever caused by ASF infection. In addition, infected animals computed significant lower movements compared to uninfected animals. The obtained results suggest that a motion monitoring system based on artificial vision may be used in indoors to trigger suspicions of fever. It would help farmers and animal health services to detect early clinical signs compatible with infectious diseases. This technology shows a promising non-intrusive, economic and real time solution in the livestock industry with especial interest in ASF, considering the current concern in the world pig industry.

Identifying Spanish Areas at More Risk of Monthly BTV Transmission with a Basic Reproduction Number Approach.

Bluetongue virus (BTV) causes a disease that is endemic in Spain and its two major biological vector species, C. imicola and the Obsoletus complex species, differ greatly in their ecology and distribution. Understanding the seasonality of BTV transmission in risk areas is key to improving surveillance and control programs, as well as to better understand the pathogen transmission networks between wildlife and livestock. Here, monthly risk transmission maps were generated using risk categories based on well-known BTV R0 equations and predicted abundances of the two most relevant vectors in Spain. Previously, Culicoides spp. predicted abundances in mainland Spain and the Balearic Islands were obtained using remote sensing data and random forest machine learning algorithm. Risk transmission maps were externally assessed with the estimated date of infection of BTV-1 and BTV-4 historical outbreaks. Our results highlight the differences in risk transmission during April-October, June-August being the period with higher R0 values. Likewise, a natural barrier has been identified between northern and central-southern areas at risk that may hamper BTV spread between them. Our results can be relevant to implement risk-based interventions for the prevention, control and surveillance of BTV and other diseases shared between livestock and wildlife host populations.

A model for the assessment of bluetongue virus serotype 1 persistence in Spain.

Bluetongue virus (BTV) is an arbovirus of ruminants that has been circulating in Europe continuously for more than two decades and has become endemic in some countries such as Spain. Spain is ideal for BTV epidemiological studies since BTV outbreaks from different sources and serotypes have occurred continuously there since 2000; BTV-1 has been reported there from 2007 to 2017. Here we develop a model for BTV-1 endemic scenario to estimate the risk of an area becoming endemic, as well as to identify the most influential factors for BTV-1 persistence. We created abundance maps at 1-km2 spatial resolution for the main vectors in Spain, Culicoides imicola and Obsoletus and Pulicaris complexes, by combining environmental satellite data with occurrence models and a random forest machine learning algorithm. The endemic model included vector abundance and host-related variables (farm density). The three most relevant variables in the endemic model were the abundance of C. imicola and Obsoletus complex and density of goat farms (AUC 0.86); this model suggests that BTV-1 is more likely to become endemic in central and southwestern regions of Spain. It only requires host- and vector-related variables to identify areas at greater risk of becoming endemic for bluetongue. Our results highlight the importance of suitable Culicoides spp. prediction maps for bluetongue epidemiological studies and decision-making about control and eradication measures.

Risk of Introduction of Infectious Animal Diseases for Europe Based on the Health Situation of North Africa and the Arabian Peninsula.

The current growth of the human population, the intensification of animal production, climate change or globalization favors an increase in the transmission of infectious diseases. Risk analysis is the tool that allows the identification of the factors involved in the introduction and the spread of infectious diseases. The main objective of this work is to evaluate the risk of entry of animal infectious zoonotic and non-zoonotic diseases from North Africa and the Arabian Peninsula to countries of the European Union. A probabilistic formulation has been developed to obtain the probabilities of introduction of diseases associated with each possible route of entry in the European Union. The results show that, among the infectious diseases analyzed in this study, avian influenza and Newcastle disease are the ones with a higher risk of entry in the European Union and the wild bird's migration is the route with greater impact. It is confirmed a moderate probability of entry of some vector-borne diseases, bluetongue and epizootic haemorrhagic disease, through wind flow from Morocco, Algeria and Tunisia. Due to the absence of live dromedary movement to Europe, the more likely way of entry of the Middle East respiratory syndrome is through the infected people movement from Saudi Arabia, Kuwait, Qatar and Oman. This study includes different methodologies. A model of vectors dispersion in wind currents has been established to assess the risk of introduction of vector borne diseases. It is applicable both in animal health and public health. A periodical update would be useful to obtain a periodically updated risk analysis and to allow early detection of potential hazard with an increased risk over the previous years.

High Load of Deformed Wing Virus and Varroa destructor Infestation Are Related to Weakness of Honey Bee Colonies in Southern Spain.

Many factors, including pathogens, contribute to the continuing losses of colonies of the honey bee Apis mellifera, which has led to steady population decline. In particular, colony losses have been linked to deformed wing virus (DWV) and the Varroa destructor mite. To clarify the potential role of these two pathogens in honey bee colony weakening and loss, we sampled colonies in southern Spain during a 21-month period and analyzed the samples for loads of four viruses and varroa. Loads of DWV and black queen cell virus as well as varroa infestation negatively correlated with colony vigor as measured using the subjective colony strength method. Logistic regression identified varroa and DWV as the main factors involved in colony weakening. Our results confirm that varroa and DWV play a key role in triggering colony weakening in southern Spain and provide evidence that experienced beekeepers' and technicians' assessments of colony vigor can accurately estimate colony strength.

The possible route of introduction of bluetongue virus serotype 3 into Sicily by windborne transportation of infected Culicoides spp.

In October 2017, the first outbreak of bluetongue virus serotype 3 (BTV-3) began in Italy, specifically in western Sicily. The route of entrance remains unclear, although since 2016 the same strain had been circulating only 150 km away, on the Tunisian peninsula of Cape Bon. The present analysis assessed the feasibility that wind could have carried BTV-3-infected Culicoides spp. from Tunisia to Sicily. An advection-deposition-survival (ADS) model was used to estimate when and where Culicoides spp. were likely to be introduced prior to the first BTV-3 report in Italy. Additionally, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to support ADS outputs. The modelling suggests that during September 2017, strong wind currents and suitable climatic conditions could have allowed the transportation of Culicoides spp. from BTV-3-infected areas in Tunisia into Sicily. ADS simulations suggest that particles could have reached the province of Trapani in western Sicily on 2 and 12 September. These simulations suggest the feasibility of aerial transportation of infected Culicoides spp. from Tunisia into Sicily. They demonstrate the suitability of the ADS model for retrospective studies of long-range transportation of insects across large water bodies, which may enhance the early detection of vectorial disease introduction in a region.

An advection-deposition-survival model to assess the risk of introduction of vector-borne diseases through the wind: Application to bluetongue outbreaks in Spain.

This work develops a methodology for estimating risk of wind-borne introduction of flying insects into a country, identifying areas and periods of high risk of vector-borne diseases incursion. This risk can be characterized by the role of suitable temperatures and wind currents in small insects' survival and movements, respectively. The model predicts the number density of introduced insects over space and time based on three processes: the advection due to wind currents, the deposition on the ground and the survival due to climatic conditions. Spanish livestock has suffered many bluetongue outbreaks since 2004 and numerous experts point to Culicoides transported by wind from affected areas in North Africa as a possible cause. This work implements numerical experiments simulating the introduction of Culicoides in 2004. The model identified southern and eastern Spain, particularly between June and November, as being at greatest risk of wind-borne Culicoides introduction, which matches field data on bluetongue outbreaks in Spain this year. This validation suggests that this model may be useful for predicting introduction of airborne pathogens of significance to animal productivity.

Body temperature and motion: Evaluation of an online monitoring system in pigs challenged with Porcine Reproductive & Respiratory Syndrome Virus.

Highly contagious and emerging diseases cause significant losses in the pig producing industry worldwide. Rapid and exact acquisition of real-time data, like body temperature and animal movement from the production facilities would enable early disease detection and facilitate adequate response. In this study, carried out within the European Union research project RAPIDIA FIELD, we tested an online monitoring system on pigs experimentally infected with the East European subtype 3 Porcine Reproductive & Respiratory Syndrome Virus (PRRSV) strain Lena. We linked data from different body temperature measurement methods and the real-time movement of the pigs. The results showed a negative correlation between body temperature and movement of the animals. The correlation was similar with both body temperature obtaining methods, rectal and thermal sensing microchip, suggesting some advantages of body temperature measurement with transponders compared with invasive and laborious rectal measuring. We also found a significant difference between motion values before and after the challenge with a virulent PRRSV strain. The decrease in motion values was noticeable before any clinical sign was recorded. Based on our results the online monitoring system could represent a practical tool in registering early warning signs of health status alterations, both in experimental and commercial production settings.

Motion-based video monitoring for early detection of livestock diseases: The case of African swine fever.

Early detection of infectious diseases can substantially reduce the health and economic impacts on livestock production. Here we describe a system for monitoring animal activity based on video and data processing techniques, in order to detect slowdown and weakening due to infection with African swine fever (ASF), one of the most significant threats to the pig industry. The system classifies and quantifies motion-based animal behaviour and daily activity in video sequences, allowing automated and non-intrusive surveillance in real-time. The aim of this system is to evaluate significant changes in animals' motion after being experimentally infected with ASF virus. Indeed, pig mobility declined progressively and fell significantly below pre-infection levels starting at four days after infection at a confidence level of 95%. Furthermore, daily motion decreased in infected animals by approximately 10% before the detection of the disease by clinical signs. These results show the promise of video processing techniques for real-time early detection of livestock infectious diseases.

Risk mapping of West Nile virus circulation in Spain, 2015.

West Nile fever is an emergent disease in Europe. The objective of this study was to conduct a predictive risk mapping of West Nile Virus (WNV) circulation in Spain based on historical data of WNV circulation. Areas of Spain with evidence of WNV circulation were mapped based on data from notifications to the surveillance systems and a literature review. A logistic regression-based spatial model was used to assess the probability of WNV circulation. Data were analyzed at municipality level. Mean temperatures of the period from June to October, presence of wetlands and presence of Special Protection Areas for birds were considered as potential predictors. Two predictors of WNV circulation were identified: higher temperature [adjusted odds ratio (AOR) 2.07, 95% CI 1.82-2.35, p<0.01] and presence of wetlands (3.37, 95% CI 1.89-5.99, p<0.01). Model validations indicated good predictions: area under the ROC curve was 0.895 (95% CI 0.870-0.919) for internal validation and 0.895 (95% CI 0.840-0.951) for external validation. This model could support improvements of WNV risk- based surveillance in Spain. The importance of a comprehensive surveillance for WNF, including human, animal and potential vectors is highlighted, which could additionally result in model refinements.

Importance of Ecological Factors and Colony Handling for Optimizing Health Status of Apiaries in Mediterranean Ecosystems.

We analyzed six apiaries in several natural environments with a Mediterranean ecosystem in Madrid, central Spain, in order to understand how landscape and management characteristics may influence apiary health and bee production in the long term. We focused on five criteria (habitat quality, landscape heterogeneity, climate, management and health), as well as 30 subcriteria, and we used the analytic hierarchy process (AHP) to rank them according to relevance. Habitat quality proved to have the highest relevance, followed by beehive management. Within habitat quality, the following subcriteria proved to be most relevant: orographic diversity, elevation range and important plant species located 1.5 km from the apiary. The most important subcriteria under beehive management were honey production, movement of the apiary to a location with a higher altitude and wax renewal. Temperature was the most important subcriterion under climate, while pathogen and Varroa loads were the most significant under health. Two of the six apiaries showed the best values in the AHP analysis and showed annual honey production of 70 and 28 kg/colony. This high productivity was due primarily to high elevation range and high orographic diversity, which favored high habitat quality. In addition, one of these apiaries showed the best value for beehive management, while the other showed the best value for health, reflected in the low pathogen load and low average number of viruses. These results highlight the importance of environmental factors and good sanitary practices to maximize apiary health and honey productivity.

Evaluation of the risk of classical swine fever (CSF) spread from backyard pigs to other domestic pigs by using the spatial stochastic disease spread model Be-FAST: the example of Bulgaria.

The study presented here is one of the very first aimed at exploring the potential spread of classical swine fever (CSF) from backyard pigs to other domestic pigs. Specifically, we used a spatial stochastic spread model, called Be-FAST, to evaluate the potential spread of CSF virus (CSFV) in Bulgaria, which holds a large number of backyards (96% of the total number of pig farms) and is one of the very few countries for which backyard pigs and farm counts are available. The model revealed that, despite backyard pigs being very likely to become infected, infections from backyard pigs to other domestic pigs were rare. In general, the magnitude and duration of the CSF simulated epidemics were small, with a median [95% PI] number of infected farms per epidemic of 1 [1,4] and a median [95% PI] duration of the epidemic of 44 [17,101] days. CSFV transmission occurs primarily (81.16%) due to indirect contacts (i.e. vehicles, people and local spread) whereas detection of infected premises was mainly (69%) associated with the observation of clinical signs on farm rather than with implementation of tracing or zoning. Methods and results of this study may support the implementation of risk-based strategies more cost-effectively to prevent, control and, ultimately, eradicate CSF from Bulgaria. The model may also be easily adapted to other countries in which the backyard system is predominant. It can also be used to simulate other similar diseases such as African swine fever.