When and why direct transmission models can be used for environmentally persistent pathogens.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

Financial Impacts of Liver Fluke on Livestock Farms Under Climate Change-A Farm Level Assessment.

Liver fluke infection (fascioliasis) is a parasitic disease which affects the health and welfare of ruminants. It is a concern for the livestock industry and is considered as a growing threat to the industry because changing climatic conditions are projected to be more favorable to increased frequency and intensity of liver fluke outbreaks. Recent reports highlighted that the incidence and geographic range of liver fluke has increased in the UK over the last decade and estimated to increase the average risk of liver fluke in the UK due to increasing temperature and rainfall. This paper explores financial impacts of the disease with and without climate change effects on Scottish livestock farms using a farm-level economic model. The model is based on farming system analysis and uses linear programming technique to maximize farm net profit within farm resources. Farm level data from a sample of 160 Scottish livestock farms is used under a no disease baseline scenario and two disease scenarios (with and without climate change). These two disease scenarios are compared with the baseline scenario to estimate the financial impact of the disease at farm levels. The results suggest a 12% reduction in net profit on an average dairy farm compared to 6% reduction on an average beef farm under standard disease conditions. The losses increase by 2-fold on a dairy farm and 6-fold on a beef farm when climate change effects are included with disease conditions on farms. There is a large variability within farm groups with profitable farms incurring relatively lesser economic losses than non-profitable farms. There is a substantial increase in number of vulnerable farms both in dairy (+20%) and beef farms (+27%) under the disease alongside climate change conditions.

Multi-host disease management: the why and the how to include wildlife.

In recent years, outbreaks caused by multi-host pathogens (MHP) have posed a serious challenge to public and animal health authorities. The frequent implication of wildlife in such disease systems and a lack of guidelines for mitigating these diseases within wild animal populations partially explain why the outbreaks are particularly challenging. To face these challenges, the French Ministry of Agriculture launched a multi-disciplinary group of experts that set out to discuss the main wildlife specific concepts in the management of MHP disease outbreaks and how to integrate wildlife in the disease management process.This position paper structures the primary specific concepts of wildlife disease management, as identified by the working group. It is designed to lay out these concepts for a wide audience of public and/or animal health officers who are not necessarily familiar with wildlife diseases. The group's discussions generated a possible roadmap for the management of MHP diseases. This roadmap is presented as a cycle for which the main successive step are: step 1-descriptive studies and monitoring; step 2-risk assessment; step 3-management goals; step 4-management actions and step 5-assessment of the management plan. In order to help choose the most adapted management actions for all involved epidemiological units, we integrated a decision-making framework (presented as a spreadsheet). This tool and the corresponding guidelines for disease management are designed to be used by public and health authorities when facing MHP disease outbreaks. These proposals are meant as an initial step towards a harmonized transboundary outbreak response framework that integrates current scientific understanding adapted to practical intervention.

Phenotypic effects of subclinical paratuberculosis (Johne's disease) in dairy cattle.

The effect of subclinical paratuberculosis (or Johne's disease) risk status on performance, health, and fertility was studied in 58,096 UK Holstein-Friesian cows with 156,837 lactations across lactations 1 to 3. Low-, medium-, and high-risk group categories were allocated to cows determined by a minimum of 4 ELISA milk tests taken at any time during their lactating life. Lactation curves of daily milk, protein, and fat yields and protein and fat percentage, together with loge-transformed somatic cell count, were estimated using a random regression model to quantify differences between risk groups. The effect of subclinical paratuberculosis risk groups on fertility, lactation-average somatic cell count, and mastitis were analyzed using linear regression fitting risk group as a fixed effect. Milk yield losses associated with high-risk cows compared with low-risk cows in lactations 1, 2, and 3 for mean daily yield were 0.34, 1.05, and 1.61kg; likewise, accumulated 305-d yields were 103, 316, and 485kg, respectively. The total loss was 904kg over the first 3 lactations. Protein and fat yield losses associated with high-risk cows were significant, but primarily a feature of decreasing milk yield. Similar trends were observed for both test-day and lactation-average somatic cell count measures with higher somatic cell counts from medium- and high-risk cows compared with low-risk cows, and differences were in almost all cases significant. Likewise, mastitis incidence was significantly higher in high-risk cows compared with low-risk cows in lactations 2 and 3. Whereas the few significant differences between risk groups among fertility traits were inconsistent with no clear trend. These results are expected to be conservative, as some animals that were considered negative may become positive after the timeframe of this study, particularly if the animal was tested when relatively young. However, the magnitude of milk yield losses together with higher somatic cell counts and an increase in mastitis incidence should motivate farmers to implement the appropriate control measures to reduce the spread of the disease.

Prioritisation of wildlife pathogens to be targeted in European surveillance programmes: Expert-based risk analysis focus on ruminants.

This study attempted to develop a list of priority pathogens. It is part of a European Union (EU) project dedicated to the surveillance of emerging or re-emerging pathogens of wildlife. Partners of the consortium established an initial list of 138 pathogens of concern, which was reduced to a smaller list of 65 pathogens likely to affect ruminants (i.e., the most costly animal group in the EU over the last 15 years). These 65 pathogens underwent a two-step, expert-based risk analysis: 92 experts graded them with respect to their global importance for animal welfare, species conservation, trade/economic impacts and public health. In step 2, the top 15 pathogens from step 1 were assessed by 69 experts considering seven weighted epidemiological criteria (pathogen variability, host specificity, potential for contagion, speed of spread, presence in Europe, difficulty of surveillance in wildlife and persistence in the environment) for which four options were possible. The responses concerned a wide geographic coverage. The resulting top-list pathogens were ranked as follows: 1. Salmonella enterica, 2. Coxiella burnetii, 3. foot-and-mouth disease virus, 4. Mycobacterium bovis, 5. bluetongue virus, and 6. European tick-borne encephalitis virus. The influence of the characteristics of the respondents, the importance of the levels of uncertainty/variability and the implication of the results are discussed. This work highlights the relevance of developing such lists for preparedness.

Risk factors for bovine Tuberculosis at the national level in Great Britain.

BACKGROUND: The continuing expansion of high incidence areas of bovine Tuberculosis (bTB) in Great Britain (GB) raises a number of questions concerning the determinants of infection at the herd level that are driving spread of the disease. Here, we develop risk factor models to quantify the importance of herd sizes, cattle imports from Ireland, history of bTB, badgers and cattle restocking in determining bTB incidence. We compare the significance of these different risk factors in high and low incidence areas (as determined by parish testing intervals). RESULTS: Large herds and fattening herds are more likely to breakdown in all areas. In areas with lower perceived risk (longer testing intervals), the risk of breaking down is largely determined by the number of animals that a herd buys in from high incidence areas. In contrast, in higher perceived risk areas (shorter testing intervals), the risk of breakdown is defined by the history of disease and the probability of badger occurrence. Despite differences in the management of bTB across different countries of GB (England, Wales and Scotland), we found no significant differences in bTB risk at the national level after these other factors had been taken into account. CONCLUSIONS: This paper demonstrates that different types of farm are at risk of breakdown and that the most important risk factors vary according to bTB incidence in an area. The results suggest that significant gains in bTB control could be made by targeting herds in low incidence areas that import the greatest number of cattle from high incidence areas.

Understanding foraging behaviour in spatially heterogeneous environments.

The role of stochasticity and spatial heterogeneity in foraging systems is investigated. We formulate a spatially explicit model which describes the behaviour of grazing animals in response to local information using simple stochastic rules. In particular the model reflects the biology in that decisions to move to a new location are based on visual assessment of the sward height in a surrounding neighbourhood, whilst the decision to graze the current location is based on the residual sward height and olfactory assessment of local faecal contamination. It is assumed that animals do not interact directly, but do so through modification of, and response to a common environment. Spatial heterogeneity is shown to have significant effects including reducing the equilibrium intake rate and increasing the optimal stocking density, and must therefore be taken into account by resource managers. We demonstrate the relationship between the stochastic spatial model and its non-spatial deterministic counterpart, and in the process derive a moment-closure approximation to the full process, which can be regarded as an intermediate, or pseudo-spatial model. The role of spatial heterogeneity is emphasized, and better understood by comparing the results obtained from each approach. The relative efficiency of random and directed searching behaviour in spatially heterogeneous environments is explored for both clean and contaminated pastures, and the impact of faecal avoidance behaviour assessed.