Satellite Derived Forest Phenology and Its Relation with Nephropathia Epidemica in Belgium

The connection between nephropathia epidemica (NE) and vegetation dynamics has been emphasized in recent studies. Changing climate has been suggested as a triggering factor of recently observed epidemiologic peaks in reported NE cases. We have investigated whether there is a connection between the NE occurrence pattern in Belgium and specific trends in remotely sensed phenology parameters of broad-leaved forests. The analysis of time series of the MODIS Enhanced Vegetation Index revealed that changes in forest phenology, considered in literature as an effect of climate change, may affect the mechanics of NE transmission

Model-based prediction of outbreak dynamics of nephropathia epidemicausing climate and vegetation data

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Fifteen emerging zoonotic or vector-borne infections with increasing impact on humans in Europe were identified during the period 2000-2006. Global climate change may be a major contributor to the spread of these zoonotic diseases. Rodent borne hantavirus infections are part of this list. Puumala virus (PUUV), hosted by the bank vole (Myodes glareolus), is such a hantavirus. It is common over vast areas of Europe and causes a general mild form hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). It is well established that climate is an important determinant of the spatial and temporal distribution of vectors (in epidemiology, a vector is any agent -person, animal or microorganism- that carries and transmits an infectious pathogen into another living organism) and pathogens. Therefore a change in climate is expected to cause changes in the geographical range, seasonality (inter annual variability) and in the incidence rate (with or without changes in geographical or seasonal patterns) of NE outbreaks. The main aim of this dissertation is in developing modelling approaches for monitoring and predicting NE outbreaks by taking into account the most significant environmental factors which affect the temporal and spatial pattern of NE cases by using compact model structures that take into account climate and vegetation data. In the chapters 2 to 6 of this dissertation we discussed in detail how data-based (mechanistic) models can be used to model and predict outbreaks of nephropatia epidemica (NE) as a basis for the development of disease prevention and control strategy. In contrast with the mechanistic modelling approach, data-based modelling techniques identify the dynamic characteristics of processes based on measured data and are as such (initially) not based on a priori process knowledge. In this dissertation, we discussed how knowledge obtained from mechanistic epidemiological population models can be used to improve the data-based model structures. In chapter 2, we discussed the importance of the carrying capacity for modelling the NE prevalence. Furthermore, we discussed the link between carrying capacity and the forest phenology which explains the possibility of predicting NE outbreaks based only on the climatological and vegetation data, without any knowledge of the bank vole’s population dynamics (chapter 3). In the second part of this thesis we described a modelling approach to predict the NE outbreaks by taking into account measured population dynamics of the bank voles only and knowledge from a mechanistic epidemiological model (Chapter 3 and 4). Human hantavirus epidemics have often been explained by bank vole abundance. Therefore in order to be able to control and prevent the occurrence of the NE cases (as an example of zoonotic disease), it is important to detect and monitor the environmental factors that affect the spatial and temporal variations of the bank vole. A method was described to produce maps of potential geographical distribution of bank voles in Western Europe based on occurrence data points of bank voles and climate information and land cover maps (chapter 5) and in chapter 6 we modelled the bank vole population dynamics in Belgium and Finland using a data-based modelling approach. The results of the current study help to define significant environmental factors on the spread of the disease. Developing a dynamic data-based mechanistic modelling approach for NE may form the basis of an expert tool to predict and prevent the incidence of NE cases by making use of remote sensing tools for measuring broad leaves forest phenology and monitoring the vegetation dynamics together with climatological data.status: publishe

Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices.

BackgroundPorcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT ji ) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight.ResultsThe model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124).ConclusionThe result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making

Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices

Abstract Background Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT ji ) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight. Results The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124). Conclusion The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making

The Automatic Monitoring of Pigs Water Use by Cameras

Every year over 59 billion animals are slaughtered for worldwide food production. The increasing demand for animal products has made mass animal breeding more important than ever. Satisfying the needs of the market, farmers will have to use automatic tools to monitor the welfare and health of their animals since manual monitoring is expensive and time consuming. Literature has shown that water use of pigs relates to important variables such as inside temperature, food intake, food conversion, growth rate and health condition. So, water use might be an interesting indicator for automatic monitoring pigs’ health or productivity status. Therefore, we tried to find a cheap and elegant way to monitor continuous water use in a group of pigs in a farm pen. This study comprised four groups of piglets, each group of ten animals in a pen. On average, in the beginning of experiments pigs had a weight of 27 kilograms and in the end they gained weight up to 40 kilograms. Using a water-meter for each pen, water use rate was measured and monitored minutely. The pig house was also equipped with Charge-coupled device (CCD) cameras. Each pen was monitored for 13 days using a camera which was installed above the pen to generate top-view images. There was a water outlet in the corner of each pen. Employing image processing algorithms, drink nipple visits were monitored automatically. Using data of a performed experiment comprising three weeks of data recordings, the relationship between water use and drink nipple visits was investigated. Results showed that by developing a data-based dynamic model of the visits to the drink nipple observed in videos, half-hourly water use could be estimated with an accuracy of 92 per cent.status: publishe

Characterization of the Temporal Trends in the Rate of Cattle Carcass Condemnations in the US and Dynamic Modeling of the Condemnation Reasons in California With a Seasonal Component

Based on the 2016 National Cattlemen's Beef Association statistics, the cattle inventory in the US reached 93.5 million head, from which 30.5 million were commercial slaughter in 2016. California ranked fourth among all the US states that raise cattle and calves, with 5.15 million head and approximately 1.18 million slaughtered animals per year. Approximately 0.5% of cattle carcasses in the US are condemned each year, which has an important economic impact on cattle producers.In this study, we first described and compared the temporal trends of cattle carcass condemnations in all the US states from Jan-2005 to Dec-2014. Then, we focused on the condemnation reasons with a seasonal component in California and used dynamic harmonic regression (DHR) models both to model (from Jan-2005 to Dec-2011) and predict (from Jan-2012 to Dec-2014) the carcass condemnations rate in different time horizons (3 to 12 months).Data consisted of daily reports of 35 condemnation reasons per cattle type reported in 684 federally inspected slaughterhouses in the US from Jan-2005 to Dec-2014 and the monthly slaughtered animals per cattle type per states. Almost 1.5 million carcasses were condemned in the US during the 10 year study period (Jan 2005-Dec 2014), and around 40% were associated with three condemnation reasons: malignant lymphoma, septicemia and pneumonia. In California, emaciation, eosinophilic myositis and malignant lymphoma were the only condemnation reasons presenting seasonality and, therefore, the only ones selected to be modeled using DHRs. The DHR models for Jan-2005 to Dec-2011 were able to correctly model the dynamics of the emaciation, malignant lymphoma and eosinophilic myositis condemnation rates with coefficient of determination (Rt2) of 0.98, 0.87 and 0.78, respectively. The DHR models for Jan-2012 to Dec-2014 were able to predict the rate of condemned carcasses 3 month ahead of time with mean relative prediction error of 33, 11, and 38%, respectively. The systematic analysis of carcass condemnations and slaughter data in a more real-time fashion could be used to identify changes in carcass condemnation trends and more timely support the implementation of prevention and mitigation strategies that reduce the number of carcass condemnations in the US

Can we monitor water use of pigs in an automated way by cameras?

Every year over 59 billion animals are slaughtered for worldwide food production. The increasing demand for animal products has made mass animal breeding more important than ever. Satisfying the needs of the market, farmers will have to use automatic tools to monitor the welfare and health of their animals since manual monitoring is expensive and time consuming. Literature has shown that water use of pigs relates to important variables such as inside temperature, food intake, food conversion, growth rate and health condition. So, water use might be an interesting indicator for automatic monitoring pigs’ health or productivity status. Therefore, we try to find a cheap and elegant way to monitor continuous water use in a group of pigs in a farm pen. This study comprises four groups of piglets, each group of ten animals in a pen. On average, in the beginning of experiments pigs had a weight of 27 kilograms and in the end they gained weight up to 40 kilograms. Using a water-meter for each pen, water use rate was measured and monitored minutely. The pig house was also equipped with Charge-coupled device (CCD) cameras. Each pen was monitored using a camera which was installed above the pen to generate top-view images. There was a water outlet in the corner of each pen. Employing image processing algorithms, drink nipple visits were monitored automatically. Using data of a performed experiment comprising three weeks of data recordings, the relationship between water use and drink nipple visits was investigated. Results show that by developing a data-based dynamic model of the visits to the drink nipple observed in videos, hourly water use can be estimated with an accuracy of 92 per cent