The North American Animal Disease Spread Model (NAADSM) is a stochastic model framework developed to simulate the spread of highly contagious diseases of livestock and poultry, such as foot-and-mouth disease and highly pathogenic avian influenza (HPAI). The objective of this study was to make recommendations on the most effective HPAI control policy for Canada, specifically, on the effect of different speeds of detection, effectiveness of movement restrictions and stamping-out and ring-culling strategies on the magnitude of an HPAI outbreak. In addition, the effect of introduction of infection in a range of multiple farms simultaneously was also evaluated. A total of 21 060 scenarios, defined as different combinations of parameters for various epidemiological conditions and control measures, were created to simulate the number of poultry flocks that would become infected as a result of an incursion of HPAI. Each scenario was parameterized in NAADSM and replicated 1000 times, generating the median number of flocks infected at the end of the simulated outbreak for each scenario. Negative binomial regression analysis was used to model significant explanatory variables of the median number of flocks infected at the end of each simulated outbreak for each of the 21 060 scenarios. The final model included the following explanatory variables: number and type initially infected flock(s), density of flocks within the county where the initially infected flock(s) was located, probability of transmission through indirect contact, subclinical spread of the infection, speed of detection and a two-way interaction between intensity of bird destruction strategy and movement restriction effectiveness to reduce transmission through direct and indirect contacts. The modelling results suggested that stamping out of the detected infected flocks, without ring culling, in combination with effective movement restrictions on direct and indirect contacts, would be the most appropriate policy for Ontario.

This study describes the use of simulation modelling to evaluate the predicted benefits of an effective livestock traceability system in responding to a hypothetical introduction of foot-and-mouth disease (FMD) in to the province of Alberta, Canada, and whether or not the implementation of emergency ring vaccination in addition to a standard stamping-out (SO) strategy would lead to smaller and shorter epidemics. Three introduction scenarios were defined, with the primary case in either an intensive beef feedlot operation, an extensive cow–calf operation or in a swine operation. Disease spread was simulated using, three levels of tracing effectiveness, five types of vaccination zone, three different vaccination start times, three lengths of vaccination campaigns, two levels of culling resource and using FMD strains with two different virulence levels. Using standard SO procedures (without vaccination), improving traceability effectiveness from a level whereby only 65% of movements were traced within 5–7 days, to a capability whereby all movements were traced within 1 day, led to a reduction in the number of infected premises (IPs) between 18.7 and 64.5%, an average saving of CAN$29,000,000 in livestock compensation costs alone, and a reduction in the length of epidemics ranging from 1 to 22 days. The implementation of emergency vaccination also led to a reduction in the number of IPs and a shortening of epidemics. The effects were more pronounced when the higher virulence settings were used, with a predicted reduction in IPs of 16.6–68.7% (mean=48.6%) and epidemics shortened by up to 37 days. Multi-variable analyses showed these effects were highly significant, after accounting for the incursion location, virulence of virus and time of first detection. The results clearly demonstrated the benefits of having effective traceability systems with rapid query and reporting functionality. The results also supported the value of early vaccination as an adjunct to SO in reducing the number of IPs and shortening the length of the epidemics. The most effective vaccination strategy involved a 3km or larger suppressive vaccination zone around all IPs, begun as soon as practicable after first detection, and which continued until the last IP was detected.

Frontline farm workers and veterinary-policy-makers are arguably in the best positions to influence prevention, detection, and preparedness-for-control of farm animal diseases. It is important that such individuals make biologically sound decisions concerning the daily management and regulation of the health of animals. Such decisions should be based on a good understanding of key principles of disease spread and control. This paper summarises these principles, as described in previous publications, into simple models. These models may be used to communicate concepts to readers who may not have time to study more complex models. These models illustrate the relationship between the development of new disease cases (from existing cases, i.e., the reproductive ratio R) and (i) the duration of the period during which existing cases are available as infectious, (ii) contact rates, (iii) transmission rates and (iv) susceptibility. Understanding these concepts through models has great utility, facilitating better decisions for disease prevention, detection and preparedness-for-control, before an outbreak becomes unmanageable. These basic concepts apply to all animal species, including humans.

Social networks analysis (SNA) has recently been used in veterinary epidemiology to study livestock movements. A network is obtained by considering livestock holdings as nodes in a network and movements among holdings as links among nodes. Social networks analysis enables the study of the network as a whole, exploring all the relationships among pairs of farms. Highly connected livestock holdings in the network can be identified, which can help surveillance and disease prevention activities. Observed livestock movement networks in various countries have shown an important level of contact heterogeneity and clustering (topological, not necessarily geographical or spatial) and understanding the architecture of these networks has provided a better understanding of how infections may spread. The findings of SNA studies of livestock movement should be used to build and parameterise epidemiological models of infection spread in order to improve the reliability of the outputs from these models.

We used the movements of adult milking cows among farms enrolled in the Dairy Herd Improvement (DHI) program in Ontario to explore the size of an epidemic that might result from farm-to-farm movements of cows in the Province if a reportable long-incubation infection like tuberculosis (TB) were introduced and not detected for 1-3 years after introduction. A directed network was created for each year (2004-2006) using all pairs of individual shipments, defined as the movement of one or more cows on a single day, from a single source DHI farm to a single recipient DHI farm. A 3-year network was also developed that included all cow shipments that took place during these 3 years. The lower and upper bounds of potential maximal epidemic size were estimated using four network-analysis measures: (1) the farm out-degree, (2) the size of the largest strong and weak components, (3) the bow-tie approach, classifying farms into six different areas of a directed network and (4) the infection chain of a farm. All four of the DHI movement networks were found to be small-world, indicating that infection could spread over considerable distances by shipments that linked potentially distant clusters of farms. The networks were also scale-free, indicating most farms had relatively few connections to other farms, while there were a few highly connected farms. Characterization of the yearly networks showed that 41-47% of DHI farms were not involved in any cow shipments and were therefore not at risk of infection from this movement network; furthermore, if infection were introduced into a DHI farm that shipped animals that year, the infection would have stopped at that farm (or at least, not been passed on by shipment of adult milking cows) >50% of the time, and 75% of the time only one more DHI farm would have become infected through animal movements. Compared to the infection chain, which accounted for both the direction and the time sequence of shipments in the movement network, the other network-analysis measures provided biased estimates of potential epidemic size. The bow-tie approach provided a schematic representation of the level of risk of each farm in the network in spreading an infection, but overestimated the lower- and upper-bound measures of potential epidemic size because it did not account for the time sequence of shipments. Our infection-chain results suggest that introducing a long-incubation disease into the network of farms enrolled in the DHI program in Ontario that was not identified until 12 months after the incursion would, in a worst-case scenario, have resulted in 168 farms (representing 5% of all Ontario DHI herds) being infected as a consequence of adult cow movements among DHI farms. This estimate increased to 850 farms (26% of all DHI herds) if the infection were not identified for 36 months.

Many countries do not have the resources to develop epidemiological models of animal diseases. As a result, it is tempting to use models developed in other countries. However, an existing model may need to be adapted in order for it to be appropriately applied in a country, region, or situation other than that for which it was originally developed. The process of adapting a model has a number of benefits for both model builders and model users. For model builders, it provides insight into the applicability of their model and potentially the opportunity to obtain data for operational validation of components of their model. For users, it is a chance to think about the infection transmission process in detail, to review the data available for modelling, and to learn the principles of epidemiological modelling. Various issues must be addressed when considering adapting a model. Most critically, the assumptions and purpose behind the model must be thoroughly understood, so that new users can determine its suitability for their situation. The process of adapting a model might simply involve changing existing model parameter values (for example, to better represent livestock demographics in a country or region), or might require more substantial (and more labour-intensive) changes to the model code and conceptual model. Adapting a model is easier if the model has a user-friendly interface and easy-to-read user documentation. In addition, models built as frameworks within which disease processes and livestock demographics and contacts are flexible are good candidates for technology transfer projects, which lead to long-term collaborations.

The papers in this issue of the Scientific and Technical Review (the Review) examine uses of modelling as a tool to supportthe formulation of disease control policy and applications of models for various aspects of animal disease management. Different issues in model development and several types of models are described. The experience with modelling during the 2001 foot and mouth disease outbreak in the United Kingdom underlines how models might be appropriately applied by decision-makers when preparing for and dealing with animal health emergencies. This paper outlines the involvement of the World Organisation for Animal Health (OIE) in epidemiological modelling since 2005, with emphasis on the outcome of the 2007 questionnaire survey of model usage among Member Countries, the subsequent OIE General Session resolution and the 2008 epidemiological modelling workshop at the Centers for Epidemiology and Animal Health in the United States. Many of the workshop presentations were developed into the papers that are presented in this issue of the Review.

Researchers from Australia, New Zealand, Canada and the United States collaborated to validate their foot and mouth disease models--AusSpread, InterSpread Plus and the North American Animal Disease Spread Model--in an effort to build confidence in their use as decision-support tools. The final stage of this project involved using the three models to simulate a number of disease outbreak scenarios, with data from the Republic of Ireland. The scenarios included an uncontrolled epidemic, and epidemics managed by combinations of stamping out and vaccination. The predicted numbers of infected premises, the duration of each epidemic, and the size of predicted outbreak areas were compared. Relative within-model between-scenario changes resulting from different control strategies or resource constraints in different scenarios were quantified and compared. Although there were differences between the models in absolute outcomes, between-scenario comparisons within each model were similar. In all three models, early use of ring vaccination resulted in the largest drop in number of infected premises compared with the standard stamping-out regimen. This consistency implies that the assumptions made by each of the three modelling teams were appropriate, which in turn serves to increase end-user confidence in predictions made by these models.

Livestock movements are important in spreading infectious diseases and many countries have developed regulations that require farmers to report livestock movements to authorities. This has led to the availability of large amounts of data for analysis and inclusion in computer simulation models developed to support policy formulation. Social network analysis has become increasingly popular to study and characterize the networks resulting from the movement of livestock from farm-to-farm and through other types of livestock operations. Network analysis is a powerful tool that allows one to study the relationships created among these operations, providing information on the role that they play in acquiring and spreading infectious diseases, information that is not readily available from more traditional livestock movement studies. Recent advances in the study of real-world complex networks are now being applied to veterinary epidemiology and infectious disease modelling and control. A review of the principles of network analysis and of the relevance of various complex network theories to infectious disease modelling and control is presented in this paper.

Adult milking cow movements occurring in monthly periods in 2004-2006 were analysed to compare three network analysis measures to determine the lower and upper bounds of potential maximal epidemic size in an unrestrained epidemic: the out-degree, the infection chain or output domain of a farm, and the size of the strong and weak components. The directed networks generated by the movements of adult milking cows were highly fragmented. When all the farms that were not involved in shipments were included in the analysis, the risk of infection transmission through movements of adult cows was very low. To determine the size of an epidemic when an infected farm shipped cows in such a fragmented network, farm out-degree and infection chain provided similar and more reasonable estimates of potential maximal epidemic size than the size of the strong and weak components. Component analysis always provided estimates that were two to three times larger than the out-degree of infection chain approaches. For example, the upper bound was estimated to be 12-13 farms using out-degree and 16-17 farms using the infection chain, the components approach showed a range of 39-51 potentially exposed farms. Strong components provided an inflated measure of the lower bound of potential maximal epidemic size at first diagnosis because the time sequence of shipments was not considered. Weak components provided an inflated measure of the upper bound because both the time sequence and directionality of shipments between farms were ignored. Farm degree and infection chain measures should now be tested to determine their usefulness for estimating maximum epidemic size in large connected networks.

The complexity and variability inherent in biological systems should limit the use of models during actual outbreaks as predictive tools. Modelling will be most useful when used pre-outbreak, particularly in the areas of retrospective analysis of previous outbreaks, contingency planning, resource planning, risk assessment and training. Models are just one tool for providing scientific advice, and results should be evaluated in conjunction with data from experimental studies, field experience and scientific wisdom.

AIMS:To describe results of a relative validation exercise using the three simulation models of foot-and-mouth disease (FMD) in use by the quadrilateral countries (QUADS; Australia, Canada, New Zealand, and United States of America; USA).METHODS:A hypothetical population of farms was constructed and, following the introduction of an FMD-like disease into a single farm, spread of disease was simulated using each of the three FMD simulation models used by the QUADS countries. A series of 11 scenarios was developed to systematically evaluate the key processes of disease transmission and control used by each of the three models. The predicted number of infected units and the size of predicted outbreak areas for each scenario and each model were compared using the Kruskal-Wallis test. Agreement among the three models in terms of geographical areas predicted to become infected were quantified using Fleiss' Kappa statistic.RESULTS:Although there were statistically significant differences in model outputs in terms of the numbers of units predicted to become infected, the temporal onset of infection throughout the simulation period, and the spatial distribution of infected units, these differences were generally small and would have resulted in the same (or similar) management decisions being adopted in each case.CONCLUSIONS:Agreement among the three models in terms of the numbers of premises predicted to become infected, the temporal onset of infection throughout the simulation period, and the spatial distribution of infected premises provides evidence that each of the model developers are consistent in their approach to simulating the spread of disease throughout a population of susceptible individuals. This consistency implies that the assumptions taken by each development team are appropriate, which in turn serves to increase end-user confidence in model predictions.CLINICAL RELEVANCE:Relative validation is one of a number of steps that can be undertaken to increase end-user confidence in predictions made by infectious disease models.

The shortage of Si feedstock and the goal of reducing W p costs in photovoltaics (PV) is the driving force to look for alternatives to ingot grown multicrystalline (me) Si wafers which have the highest share in the PV market. Ribbon Si seems to be a very promising candidate as no kerf losses occur, resulting in reduced Si costs per W p . In addition, there is no need for the energy consuming crystallization of the ingot and therefore energy payback times can be significantly reduced. The higher defect density in ribbon Si materials has to be taken into account during cell processing, but ribbon materials already commercially available show excellent efficiencies, while for the most promising techniques efficiencies are significantly lower, but very promising. In this presentation an overview of ribbon Si technologies currently under research will be given, based on available data on crystal growth as well as solar cell processing and cell parameters

Multicrystalline ribbon silicon solar cells made from String Ribbon material have a high potential to bring down Watt-peak costs in photovoltaic. This is mainly because of the inherent advantages linked with the growth technique like the excellent silicon usage and the lack of a saw damage due to wafer cutting. On the other hand it is difficult to apply an efficient surface texture to the undamaged wafer surface. We tried to adapt existing acidic surface textures to the String Ribbon material needs and evaluated their effectiveness on reflectivity as well as solar cell efficiency level. In a first approach using existing standard etch solutions we could not observe beneficial effects on Jsc or η due to a preferential etching of grain boundaries. Advanced developments of new textures lead to a more homogeneous texture pattern and first results on solar cell level seem to be promising as a slight increase in Jsc could be observed. These new textures have to be developed further to significantly increase η of textured cells.

Transient segregation with second phase formation is studied for silicon sheet crystals grown from melts deliberately doped with iron and with chromium. Normal transients are modified by the second phase already after a few cm into growth. Second phases of iron and chromium appear in the form of spherical particles, sheet-like lamellae and filaments. Experimental data on the transient behavior and morphology of the second phases are related to segregation with eutectic phase formation.

Hydrogen passivation using a Kaufmann ion source at 400°C has been carried out on FZ and CZ silicon dislocated by four-point bending at high temperatures. The results differ from those reported for dislocations passivated in silicon sheet grown by the EFG technique. A model for hydrogen diffusion and trapping is presented to argue that the differences observed are not produced by hydrogen transport effects in the bulk.