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Human salmonellosis cases are often caused by Salmonella serovars Enteritidis and Typhimurium and associated with the consumption of eggs and egg products. Many countries therefore implemented general surveillance programmes on pullet and layer farms. The identification of risk factors for Salmonella infection may be used to improve the performance of these surveillance programmes. The aims of this study were therefore to determine 1) whether local farm density is a risk factor for the infection of pullet and layer farms by Salmonella Enteritidis and Typhimurium and 2) whether the sampling effort of surveillance programmes can be reduced by accounting for this risk factor, while still providing sufficient control of these serovars. We assessed the importance of local farm density as a risk factor by fitting transmission kernels to Israeli surveillance data during the period from June 2017 to April 2019. The analysis shows that the risk of infection by serovars Enteritidis and Typhimurium significantly increased if infected farms were present within a radius of approximately 4 km and 0.3 km, respectively. We subsequently optimized a surveillance programme that subdivided layer farms into low and high risk groups based on the local farm density with and allowed the sampling frequency to vary between these groups. In this design, the pullet farms were always sampled one week prior to pullet distribution. Our analysis shows that the risk-based surveillance programme is able to keep the between-farm R0 of serovars Enteritidis and Typhimurium below 1 for all pullet and layer farms, while reducing the sampling effort by 32% compared to the currently implemented surveillance programme in Israel. The results of our study therefore indicate that local farm density is an important risk factor for infection of pullet and layer farms by Salmonella Enteritidis and Typhimurium and can be used to improve the performance of surveillance programmes.

Between 2007 and 2010 a Q fever epidemic in Dutch dairy goat farms caused a large Q fever outbreak in human residents in the southern part of the Netherlands. Here we characterize the transmission of Coxiella burnetii, the aetiological agent of Q fever, between infected and susceptible dairy goat farms by estimating a spatial transmission kernel. In addition, we characterize the zoonotic transmission of C. burnetii by estimating the spatial kernel for transmission from infected farms to neighbouring residents. Whereas the range of between-farm transmission is comparable to the scale of the Netherlands, likely due to long-range between-farm contacts such as animal transport, the transmission risk from farms to humans is more localized, although still extending to 10 km and beyond. Within a range of about 10 km, the transmission risk from an infected goat farm to a single resident is of the same order of magnitude as the farm-to-farm transmission risk per animal in a receiving farm. We illustrate how, based on the estimated kernels, spatial patterns of transmission risks between farms and from farms to residents can be calculated and visualized by means of risk maps, offering further insight relevant to policy making in a one-health context.

To evaluate and compare the risk of emerging vector-borne diseases (VBDs), a Model for INTegrated RISK assessment, MINTRISK, was developed to assess the introduction risk of VBDs for new regions in an objective, transparent and repeatable manner. MINTRISK is a web-based calculation tool, that provides semi-quantitative risk scores that can be used for prioritization purposes. Input into MINTRISK is entered by answering questions regarding entry, transmission, establishment, spread, persistence and impact of a selected VBD. Answers can be chosen from qualitative answer categories with accompanying quantitative explanation to ensure consistent answering. The quantitative information is subsequently used as input for the model calculations to estimate the risk for each individual step in the model and for the summarizing output values (rate of introduction; epidemic size; overall risk). The risk assessor can indicate his uncertainty on each answer, and this is accounted for by Monte Carlo simulation. MINTRISK was used to assess the risk of four VBDs (African horse sickness, epizootic haemorrhagic disease, Rift Valley fever, and West Nile fever) for the Netherlands with the aim to prioritise these diseases for preparedness. Results indicated that the overall risk estimate was very high for all evaluated diseases but epizootic haemorrhagic disease. Uncertainty intervals were, however, wide limiting the options for ranking of the diseases. Risk profiles of the VBDs differed. Whereas all diseases were estimated to have a very high economic impact once introduced, the estimated introduction rates differed from low for Rift Valley fever and epizootic haemorrhagic disease to moderate for African horse sickness and very high for West Nile fever. Entry of infected mosquitoes on board of aircraft was deemed the most likely route of introduction for West Nile fever into the Netherlands, followed by entry of infected migratory birds.

BACKGROUND: Bluetongue (BT) is a vector-borne disease of ruminants caused by bluetongue virus that is transmitted by biting midges (Culicoides spp.). In 2006, the introduction of BTV serotype 8 (BTV-8) caused a severe epidemic in Western and Central Europe. The principal effective veterinary measure in response to BT was believed to be vaccination accompanied by other measures such as movement restrictions and surveillance. As the number of vaccine doses available at the start of the vaccination campaign was rather uncertain, the Dutch Ministry of Agriculture, Nature and Food Quality and the Dutch agricultural industry wanted to evaluate several different vaccination strategies. This study aimed to rank eight vaccination strategies based on their efficiency (i.e. net costs in relation to prevented losses or benefits) for controlling the bluetongue virus serotype 8 epidemic in 2008. METHODOLOGY/PRINCIPAL FINDINGS: An economic model was developed that included the Dutch professional cattle, sheep and goat sectors together with the hobby farms. Strategies were evaluated based on the least cost - highest benefit frontier, the benefit-cost ratio and the total net returns. Strategy F, where all adult sheep at professional farms in The Netherlands would be vaccinated was very efficient at lowest costs, whereas strategy D, where additional to all adult sheep at professional farms also all adult cattle in the four Northern provinces would be vaccinated, was also very efficient but at a little higher costs. Strategy C, where all adult sheep and cattle at professional farms in the whole of The Netherlands would be vaccinated was also efficient but again at higher costs. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that a financial analysis differentiates between vaccination strategies and indicates important decision rules based on efficiency.