Cattle farmer psychosocial profiles and their association with control strategies for bovine viral diarrhea.

Bovine viral diarrhea (BVD) is endemic in the United Kingdom and causes major economic losses. Control is largely voluntary for individual farmers and is likely to be influenced by psychosocial factors, such as altruism, trust, and psychological proximity (feeling close) to relevant "others," such as farmers, veterinarians, the government, and their cows. These psychosocial factors (factors with both psychological and social aspects) are important determinants of how people make decisions related to their own health, many of which have not been studied in the context of infectious disease control by farmers. Farmer psychosocial profiles were investigated using multiple validated measures in an observational survey of 475 UK cattle farmers using the capability, opportunity, motivation-behavior (COM-B) framework. Farmers were clustered by their BVD control practices using latent class analysis. Farmers were split into 5 BVD control behavior classes, which were tested for associations with the psychosocial and COM-B factors using multinomial logistic regression, with doing nothing as the baseline class. Farmers who were controlling disease both for themselves and others were more likely to do something to control BVD (e.g., test, vaccinate). Farmers who did not trust other farmers, had high psychological capability (knowledge and understanding of how to control disease), and had high physical opportunity (time and money to control disease) were more likely to have a closed, separate herd and test. Farmers who did not trust other farmers were also more likely to undertake many prevention strategies with an open herd. Farmers with high automatic motivation (habits and emotions) and reflective motivation (decisions and goals) were more likely to vaccinate and test, alone or in combination with other controls. Farmers with high psychological proximity (feeling of closeness) to their veterinarian were more likely to undertake many prevention strategies in an open herd. Farmers with high psychological proximity to dairy farmers and low psychological proximity to beef farmers were more likely to keep their herd closed and separate and test or vaccinate and test. Farmers who had a lot of trust in other farmers and invested in them, rather than keeping everything for themselves, were more likely to be careful introducing new stock and test. In conclusion, farmer psychosocial factors were associated with strategies for BVD control in UK cattle farmers. Psychological proximity to veterinarians was a novel factor associated with proactive BVD control and was more important than the more extensively investigated trust. These findings highlight the importance of a close veterinarian-farmer relationship and are important for promoting effective BVD control by farmers, which has implications for successful nationwide BVD control and eradication schemes.

The Role of Movement Restrictions in Limiting the Economic Impact of Livestock Infections.

Movements are essential for the economic success of the livestock industry. These movements however bring the risk of long-range spread of infection, potentially bringing infection to previously disease-free areas where subsequent localised transmission can be devastating. Mechanistic predictive models usually consider controls that minimize the number of livestock affected without considering other costs of an ongoing epidemic. However, it is more appropriate to consider the economic burden, as movement restrictions have major consequences for the economic revenue of farms. Using mechanistic models of foot-and-mouth disease (FMD), bluetongue virus (BTV) and bovine tuberculosis (bTB) in the UK, we contrast the economically optimal control strategies for these diseases. We show that for FMD, the optimal strategy is to ban movements in a small radius around infected farms; the balance between disease control and maintaining 'business as usual' varies between regions. For BTV and bTB, we find that the cost of any movement ban is more than the epidemiological benefits due to the low within-farm prevalence and slow rate of disease spread. This work suggests that movement controls need to be carefully matched to the epidemiological and economic consequences of the disease, and optimal movement bans are often far shorter than existing policy.

Support needs and experiences of family members of wounded, injured or sick UK service personnel.

INTRODUCTION: When a service person has been wounded, injured or sick (WIS), family members may provide care during their recovery in an unpaid capacity. This may occur in diverse environments including hospitals, inpatient rehabilitation centres, in the community and at home. METHOD: Thirty-seven family members of WIS personnel were interviewed regarding their support needs, family relationships and use of UK support services. Semistructured, in-depth telephone interviews were used, with data analysis undertaken using a thematic approach. RESULTS: 'Family member involvement' was the main theme under which four subthemes were situated: 'continuity of support', 'proactive signposting and initiating contact', 'psychoeducation and counselling' and 'higher risk groups'. Family members felt they might benefit from direct, consistent and continuous care regardless of the WIS person's injury or engagement type, and whether the WIS person was being treated in a hospital, rehabilitative centre or at home. CONCLUSION: The findings of this study suggest that family members of WIS personnel value proactive, direct and sustained communication from support service providers. We suggest that families of UK service personnel may benefit from family care coordinators, who could provide continuous and consistent care to family members of WIS personnel.

Data-driven models to predict the elimination of sleeping sickness in former Equateur province of DRC.

Approaching disease elimination, it is crucial to be able to assess progress towards key objectives using quantitative tools. For Gambian human African trypanosomiasis (HAT), the ultimate goal is to stop transmission by 2030, while intermediary targets include elimination as a public health problem - defined as <1 new case per 10,000 inhabitants in 90% of foci, and <2000 reported cases by 2020. Using two independent mathematical models, this study assessed the achievability of these goals in the former Equateur province of the Democratic Republic of Congo, which historically had endemic levels of disease. The two deterministic models used different assumptions on disease progression, risk of infection and non-participation in screening, reflecting biological uncertainty. To validate the models a censor-fit-uncensor procedure was used to fit to health-zone level data from 2000 to 2012; initially the last six years were censored, then three and the final step utilised all data. The different model projections were used to evaluate the expected transmission and reporting for each health zone within each province under six intervention strategies using currently available tools. In 2012 there were 197 reported HAT cases in former Equateur reduced from 6828 in 2000, however this reflects lower active testing for HAT (1.3% of the population compared to 7.2%). Modelling results indicate that there are likely to be <300 reported cases in former Equateur in 2020 if screening continues at the mean level for 2000-2012 (6.2%), and <120 cases if vector control is introduced. Some health zones may fail to achieve <1 new case per 10,000 by 2020 without vector control, although most appear on track for this target using medical interventions alone. The full elimination goal will be harder to reach; between 39 and 54% of health zones analysed may have to improve their current medical-only strategy to stop transmission completely by 2030.

Fractal measures of spatial pattern as a heuristic for return rate in vegetative systems.

Measurement of population persistence is a long-standing problem in ecology; in particular, whether it is possible to gain insights into persistence without long time-series. Fractal measurements of spatial patterns, such as the Korcak exponent or boundary dimension, have been proposed as indicators of the persistence of underlying dynamics. Here we explore under what conditions a predictive relationship between fractal measures and persistence exists. We combine theoretical arguments with an aerial snapshot and time series from a long-term study of seagrass. For this form of vegetative growth, we find that the expected relationship between the Korcak exponent and persistence is evident at survey sites where the population return rate can be measured. This highlights a limitation of the use of power-law patch-size distributions and other indicators based on spatial snapshots. Moreover, our numeric simulations show that for a single species and a range of environmental conditions that the Korcak-persistence relationship provides a link between temporal dynamics and spatial pattern; however, this relationship is specific to demographic factors, so we cannot use this methodology to compare between species.

UK support services for families of wounded, injured or sick Service personnel: the need for evaluation.

Concerns have been raised about the level of support available to those families who provide unpaid care for wounded, injured or sick (WIS) personnel in the UK. In this opinion piece, support services for families of WIS personnel in the UK and their associated evaluations are reviewed. Support services are found to be provided by government-led initiatives, third sector organisations and the Service Family Federations. A minority of interventions for families of WIS personnel in the UK have been evaluated and a culture of service evaluation is encouraged within the field.

Aggregation dynamics explain vegetation patch-size distributions.

Vegetation patch-size distributions have been an intense area of study for theoreticians and applied ecologists alike in recent years. Of particular interest is the seemingly ubiquitous nature of power-law patch-size distributions emerging in a number of diverse ecosystems. The leading explanation of the emergence of these power-laws is due to local facilitative mechanisms. There is also a common transition from power law to exponential distribution when a system is under global pressure, such as grazing or lack of rainfall. These phenomena require a simple mechanistic explanation. Here, we study vegetation patches from a spatially implicit, patch dynamic viewpoint. We show that under minimal assumptions a power-law patch-size distribution appears as a natural consequence of aggregation. A linear death term also leads to an exponential term in the distribution for any non-zero death rate. This work shows the origin of the breakdown of the power-law under increasing pressure and shows that in general, we expect to observe a power law with an exponential cutoff (rather than pure power laws). The estimated parameters of this distribution also provide insight into the underlying ecological mechanisms of aggregation and death.

Mental health stigmatisation in deployed UK Armed Forces: a principal components analysis.

INTRODUCTION: UK military research suggests that there is a significant link between current psychological symptoms, mental health stigmatisation and perceived barriers to care (stigma/BTC). Few studies have explored the construct of stigma/BTC in depth amongst deployed UK military personnel. METHOD: Three survey datasets containing a stigma/BTC scale obtained during UK deployments to Iraq and Afghanistan were combined (n=3405 personnel). Principal component analysis was used to identify the key components of stigma/BTC. The relationship between psychological symptoms, the stigma/BTC components and help seeking were examined. RESULTS: Two components were identified: 'potential loss of personal military credibility and trust' (stigma Component 1, five items, 49.4% total model variance) and 'negative perceptions of mental health services and barriers to help seeking' (Component 2, six items, 11.2% total model variance). Component 1 was endorsed by 37.8% and Component 2 by 9.4% of personnel. Component 1 was associated with both assessed and subjective mental health, medical appointments and admission to hospital. Stigma Component 2 was associated with subjective and assessed mental health but not with medical appointments. Neither component was associated with help-seeking for subjective psycho-social problems. CONCLUSIONS: Potential loss of credibility and trust appeared to be associated with help-seeking for medical reasons but not for help-seeking for subjective psychosocial problems. Those experiencing psychological symptoms appeared to minimise the effects of stigma by seeking out a socially acceptable route into care, such as the medical consultation, whereas those who experienced a subjective mental health problem appeared willing to seek help from any source.

Age- and bite-structured models for vector-borne diseases.

The biology and behaviour of biting insects is a vitally important aspect in the spread of vector-borne diseases. This paper aims to determine, through the use of mathematical models, what effect incorporating vector senescence and realistic feeding patterns has on disease. A novel model is developed to enable the effects of age- and bite-structure to be examined in detail. This original PDE framework extends previous age-structured models into a further dimension to give a new insight into the role of vector biting and its interaction with vector mortality and spread of disease. Through the PDE model, the roles of the vector death and bite rates are examined in a way which is impossible under the traditional ODE formulation. It is demonstrated that incorporating more realistic functions for vector biting and mortality in a model may give rise to different dynamics than those seen under a more simple ODE formulation. The numerical results indicate that the efficacy of control methods that increase vector mortality may not be as great as predicted under a standard host-vector model, whereas other controls including treatment of humans may be more effective than previously thought.

Eight challenges in modelling infectious livestock diseases.

The transmission of infectious diseases of livestock does not differ in principle from disease transmission in any other animals, apart from that the aim of control is ultimately economic, with the influence of social, political and welfare constraints often poorly defined. Modelling of livestock diseases suffers simultaneously from a wealth and a lack of data. On the one hand, the ability to conduct transmission experiments, detailed within-host studies and track individual animals between geocoded locations make livestock diseases a particularly rich potential source of realistic data for illuminating biological mechanisms of transmission and conducting explicit analyses of contact networks. On the other hand, scarcity of funding, as compared to human diseases, often results in incomplete and partial data for many livestock diseases and regions of the world. In this overview of challenges in livestock disease modelling, we highlight eight areas unique to livestock that, if addressed, would mark major progress in the area.

Perceptions of the impact a military career has on children.

BACKGROUND: The perceived effects of a military career on service personnel's children have been largely overlooked. AIMS: To examine the views of military personnel about the impact their career has on their children in relation to socio-demographic variables, military characteristics and mental health symptoms. METHODS: Service personnel (regular and reserve) with one or more children (<18 years) were included. Data were taken from a large UK military cohort study completed between 2007 and 2009. Participants were asked to report whether they viewed their military career as having a positive, negative or no impact on their children. RESULTS: There were 3198 participants. Just over half (51%) of service personnel perceived their military career as having a negative impact on their children. Not being in a relationship (multinomial odds ratio 2.65, 95% CI 1.81-3.88), deployment for 13 months or more within a 3 year period (1.85, 1.31-2.62), symptoms of common mental health disorder (2.21, 1.65-2.96) and probable post-traumatic stress disorder (3.26, 1.39-7.66) were associated with perceiving military career as affecting children negatively. Reserves were less likely than regulars (0.37, 0.27-0.51) and other ranks were less likely than non-commissioned officers (0.67, 0.46-0.98) to report negative effects of their military career on their children. CONCLUSIONS: Contrary to previous research findings, regulars were more likely to report a negative impact, reflecting this study's focus on the wider military context, rather than just deployment. These findings are consistent with existing research showing links between deployment length and negative impact.

Epidemiological consequences of household-based antiviral prophylaxis for pandemic influenza.

Antiviral treatment offers a fast acting alternative to vaccination; as such it is viewed as a first-line of defence against pandemic influenza in protecting families and households once infection has been detected. In clinical trials, antiviral treatments have been shown to be efficacious in preventing infection, limiting disease and reducing transmission, yet their impact at containing the 2009 influenza A(H1N1)pdm outbreak was limited. To understand this seeming discrepancy, we develop a general and computationally efficient model for studying household-based interventions. This allows us to account for uncertainty in quantities relevant to the 2009 pandemic in a principled way, accounting for the heterogeneity and variability in each epidemiological process modelled. We find that the population-level effects of delayed antiviral treatment and prophylaxis mean that their limited overall impact is quantitatively consistent (at current levels of precision) with their reported clinical efficacy under ideal conditions. Hence, effective control of pandemic influenza with antivirals is critically dependent on early detection and delivery ideally within 24 h.

Contrasting beliefs about screening for mental disorders among UK military personnel returning from deployment to Afghanistan.

OBJECTIVE: The objective of the study was to elicit beliefs and experiences of the value of a screening programme for mental illness among UK military personnel. METHOD: Three months after returning from Afghanistan 21 army personnel participated in a qualitative study about mental health screening. One-to-one interviews were conducted and recorded. Data-driven thematic analysis was used. Researchers identified master themes represented by extracts of text from the 21 complete transcripts. RESULTS: Participants made positive remarks on the advantages of screening. Noted barriers to seeking help included: unwillingness to receive advice, a wish to deal with any problems themselves and a belief that military personnel should be strong enough to cope with any difficulties. Participants believed that overcoming barriers to participating in screening and seeking help would be best achieved by making screening compulsory. CONCLUSIONS: Although respondents were positive about a screening programme for mental illness, the barriers to seeking help for mental illness appear deep rooted and reinforced by the value ascribed to hardiness.

Mathematical modelling of infectious diseases.

INTRODUCTION: Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Here, we illustrate these principles in relation to the current H1N1 epidemic. SOURCES OF DATA: Many sources of data are used in mathematical modelling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important. AREAS OF AGREEMENT: Mathematical models, and the statistical tools that underpin them, are now a fundamental element in planning control and mitigation measures against any future epidemic of an infectious disease. Well-parameterized mathematical models allow us to test a variety of possible control strategies in computer simulations before applying them in reality. AREAS OF CONTROVERSY: The interaction between modellers and public-health practitioners and the level of detail needed for models to be of use. GROWING POINTS: The need for stronger statistical links between models and data. AREAS TIMELY FOR DEVELOPING RESEARCH: Greater appreciation by the medical community of the uses and limitations of models and a greater appreciation by modellers of the constraints on public-health resources.

Efficient methods for studying stochastic disease and population dynamics.

Stochastic ecological and epidemiological models are now routinely used to inform management and decision making throughout conservation and public-health. A difficulty with the use of such models is the need to resort to simulation methods when the population size (and hence the size of the state space) becomes large, resulting in the need for a large amount of computation to achieve statistical confidence in results. Here we present two methods that allow evaluation of all quantities associated with one- (and higher) dimensional Markov processes with large state spaces. We illustrate these methods using SIS disease dynamics and studying species that are affected by catastrophic events. The methods allow the possibility of extending exact Markov methods to real-world problems, providing techniques for efficient parameterisation and subsequent analysis.

Implications of vaccination and waning immunity.

For infectious diseases where immunization can offer lifelong protection, a variety of simple models can be used to explain the utility of vaccination as a control method. However, for many diseases, immunity wanes over time and is subsequently enhanced (boosted) by asymptomatic encounters with the infection. The study of this type of epidemiological process requires a model formulation that can capture both the within-host dynamics of the pathogen and immune system as well as the associated population-level transmission dynamics. Here, we parametrize such a model for measles and show how vaccination can have a range of unexpected consequences as it reduces the natural boosting of immunity as well as reducing the number of naive susceptibles. In particular, we show that moderate waning times (40-80 years) and high levels of vaccination (greater than 70%) can induce large-scale oscillations with substantial numbers of symptomatic cases being generated at the peak. In addition, we predict that, after a long disease-free period, the introduction of infection will lead to far larger epidemics than that predicted by standard models. These results have clear implications for the long-term success of any vaccination campaign and highlight the need for a sound understanding of the immunological mechanisms of immunity and vaccination.

Predicting undetected infections during the 2007 foot-and-mouth disease outbreak.

Active disease surveillance during epidemics is of utmost importance in detecting and eliminating new cases quickly, and targeting such surveillance to high-risk individuals is considered more efficient than applying a random strategy. Contact tracing has been used as a form of at-risk targeting, and a variety of mathematical models have indicated that it is likely to be highly efficient. However, for fast-moving epidemics, resource constraints limit the ability of the authorities to perform, and follow up, contact tracing effectively. As an alternative, we present a novel real-time Bayesian statistical methodology to determine currently undetected (occult) infections. For the UK foot-and-mouth disease (FMD) epidemic of 2007, we use real-time epidemic data synthesized with previous knowledge of FMD outbreaks in the UK to predict which premises might have been infected, but remained undetected, at any point during the outbreak. This provides both a framework for targeting surveillance in the face of limited resources and an indicator of the current severity and spatial extent of the epidemic. We anticipate that this methodology will be of substantial benefit in future outbreaks, providing a compromise between targeted manual surveillance and random or spatially targeted strategies.

Household structure and infectious disease transmission.

One of the central tenets of modern infectious disease epidemiology is that an understanding of heterogeneities, both in host demography and transmission, allows control to be efficiently optimized. Due to the strong interactions present, households are one of the most important heterogeneities to consider, both in terms of predicting epidemic severity and as a target for intervention. We consider these effects in the context of pandemic influenza in Great Britain, and find that there is significant local (ward-level) variation in the basic reproductive ratio, with some regions predicted to suffer 50% faster growth rate of infection than the mean. Childhood vaccination was shown to be highly effective at controlling an epidemic, generally outperforming random vaccination and substantially reducing the variation between regions; only nine out of over 10 000 wards did not obey this rule and these can be identified as demographically atypical regions. Since these benefits of childhood vaccination are a product of correlations between household size and number of dependent children in the household, our results are qualitatively robust for a variety of disease scenarios.

Integrating stochasticity and network structure into an epidemic model.

While the foundations of modern epidemiology are based upon deterministic models with homogeneous mixing, it is being increasingly realized that both spatial structure and stochasticity play major roles in shaping epidemic dynamics. The integration of these two confounding elements is generally ascertained through numerical simulation. Here, for the first time, we develop a more rigorous analytical understanding based on pairwise approximations to incorporate localized spatial structure and diffusion approximations to capture the impact of stochasticity. Our results allow us to quantify, analytically, the impact of network structure on the variability of an epidemic. Using the susceptible-infectious-susceptible framework for the infection dynamics, the pairwise stochastic model is compared with the stochastic homogeneous-mixing (mean-field) model--although to enable a fair comparison the homogeneous-mixing parameters are scaled to give agreement with the pairwise dynamics. At equilibrium, we show that the pairwise model always displays greater variation about the mean, although the differences are generally small unless the prevalence of infection is low. By contrast, during the early epidemic growth phase when the level of infection is increasing exponentially, the pairwise model generally shows less variation.

On methods for studying stochastic disease dynamics.

Models that deal with the individual level of populations have shown the importance of stochasticity in ecology, epidemiology and evolution. An increasingly common approach to studying these models is through stochastic (event-driven) simulation. One striking disadvantage of this approach is the need for a large number of replicates to determine the range of expected behaviour. Here, for a class of stochastic models called Markov processes, we present results that overcome this difficulty and provide valuable insights, but which have been largely ignored by applied researchers. For these models, the so-called Kolmogorov forward equation (also called the ensemble or master equation) allows one to simultaneously consider the probability of each possible state occurring. Irrespective of the complexities and nonlinearities of population dynamics, this equation is linear and has a natural matrix formulation that provides many analytical insights into the behaviour of stochastic populations and allows rapid evaluation of process dynamics. Here, using epidemiological models as a template, these ensemble equations are explored and results are compared with traditional stochastic simulations. In addition, we describe further advantages of the matrix formulation of dynamics, providing simple exact methods for evaluating expected eradication (extinction) times of diseases, for comparing expected total costs of possible control programmes and for estimation of disease parameters.