Interaction of Human Behavioral Factors Shapes the Transmission of Arboviruses by Aedes and Culex Mosquitoes

Arboviruses, i.e., viruses transmitted by blood-sucking arthropods, trigger significant global epidemics. Over the past 20 years, the frequency of the (re-)emergence of these pathogens, particularly those transmitted by Aedes and Culex mosquitoes, has dramatically increased. Therefore, understanding how human behavior is modulating population exposure to these viruses is of particular importance. This synthesis explores human behavioral factors driving human exposure to arboviruses, focusing on household surroundings, socio-economic status, human activities, and demographic factors. Household surroundings, such as the lack of water access, greatly influence the risk of arbovirus exposure by promoting mosquito breeding in stagnant water bodies. Socio-economic status, such as low income or low education, is correlated to an increased incidence of arboviral infections and exposure. Human activities, particularly those practiced outdoors, as well as geographical proximity to livestock rearing or crop cultivation, inadvertently provide favorable breeding environments for mosquito species, escalating the risk of virus exposure. However, the effects of demographic factors like age and gender can vary widely through space and time. While climate and environmental factors crucially impact vector development and viral replication, household surroundings, socio-economic status, human activities, and demographic factors are key drivers of arbovirus exposure. This article highlights that human behavior creates a complex interplay of factors influencing the risk of mosquito-borne virus exposure, operating at different temporal and spatial scales. To increase awareness among human populations, we must improve our understanding of these complex factors.

Identifying target areas for risk-based surveillance and control of transboundary animal diseases: a seasonal analysis of slaughter and live-trade cattle movements in Uganda

Animal movements are a major driver for the spread of Transboundary Animal Diseases (TADs). These movements link populations that would otherwise be isolated and hence create opportunities for susceptible and infected individuals to meet. We used social network analysis to describe the seasonal network structure of cattle movements in Uganda and unravel critical network features that identify districts or sub-regions for targeted risk-based surveillance and intervention. We constructed weighted, directed networks based on 2019 between-district cattle movements using official livestock mobility data; the purpose of the movement ('slaughter' vs. 'live trade') was used to subset the network and capture the risks more reliably. Our results show that cattle trade can result in local and long-distance disease spread in Uganda. Seasonal variability appears to impact the structure of the network, with high heterogeneity of node and edge activity identified throughout the seasons. These observations mean that the structure of the live trade network can be exploited to target influential district hubs within the cattle corridor and peripheral areas in the south and west, which would result in rapid network fragmentation, reducing the contact structure-related trade risks. Similar exploitable features were observed for the slaughter network, where cattle traffic serves mainly slaughter hubs close to urban centres along the cattle corridor. Critically, analyses that target the complex livestock supply value chain offer a unique framework for understanding and quantifying risks for TADs such as Foot-and-Mouth disease in a land-locked country like Uganda. These findings can be used to inform the development of risk-based surveillance strategies and decision making on resource allocation. For instance, vaccine deployment, biosecurity enforcement and capacity building for stakeholders at the local community and across animal health services with the potential to limit the socio-economic impact of outbreaks, or indeed reduce their frequency.

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted, and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.

A scoping review of foot-and-mouth disease risk, based on spatial and spatio-temporal analysis of outbreaks in endemic settings

Foot-and-mouth disease (FMD) is one of the most important transboundary animal diseases affecting livestock and wildlife species worldwide. Sustained viral circulation, as evidenced by serological surveys and the recurrence of outbreaks, suggests endemic transmission cycles in some parts of Africa, Asia and the Middle East. This is the result of a complex process in which multiple serotypes, multi-host interactions and numerous socio-epidemiological factors converge to facilitate disease introduction, survival and spread. Spatial and spatio-temporal analyses have been increasingly used to explore the burden of the disease by identifying high-risk areas, analysing temporal trends and exploring the factors that contribute to the outbreaks. We systematically retrieved spatial and spatial-temporal studies on FMD outbreaks to summarize variations on their methodological approaches and identify the epidemiological factors associated with the outbreaks in endemic contexts. Fifty-one studies were included in the final review. A high proportion of papers described and visualized the outbreaks (72.5%) and 49.0% used one or more approaches to study their spatial, temporal and spatio-temporal aggregation. The epidemiological aspects commonly linked to FMD risk are broadly categorizable into themes such as (a) animal demographics and interactions, (b) spatial accessibility, (c) trade, (d) socio-economic and (e) environmental factors. The consistency of these themes across studies underlines the different pathways in which the virus is sustained in endemic areas, with the potential to exploit them to design tailored evidence based-control programmes for the local needs. There was limited data linking the socio-economics of communities and modelled FMD outbreaks, leaving a gap in the current knowledge. A thorough analysis of FMD outbreaks requires a systemic view as multiple epidemiological factors contribute to viral circulation and may improve the accuracy of disease mapping. Future studies should explore the links between socio-economic and epidemiological factors as a foundation for translating the identified opportunities into interventions to improve the outcomes of FMD surveillance and control initiatives in endemic contexts.

FAIR data pipeline: provenance-driven data management for traceable scientific workflows

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Developing digital contact tracing tailored to haulage in East Africa to support COVID-19 surveillance: a protocol

INTRODUCTION

At the peak of Uganda's first wave of SARS-CoV-2 in May 2020, one in three COVID-19 cases was linked to the haulage sector. This triggered a mandatory requirement for a negative PCR test result at all ports of entry and exit, resulting in significant delays as haulage drivers had to wait for 24-48 hours for results, which severely crippled the regional supply chain.To support public health and economic recovery, we aim to develop and test a mobile phone-based digital contact tracing (DCT) tool that both augments conventional contact tracing and also increases its speed and efficiency.

METHODS AND ANALYSIS

To test the DCT tool, we will use a stratified sample of haulage driver journeys, stratified by route type (regional and local journeys).We will include at least 65% of the haulage driver journeys ~83 200 on the network through Uganda. This allows us to capture variations in user demographics and socioeconomic characteristics that could influence the use and adoption of the DCT tool. The developed DCT tool will include a mobile application and web interface to collate and intelligently process data, whose output will support decision-making, resource allocation and feed mathematical models that predict epidemic waves.The main expected result will be an open source-tested DCT tool tailored to haulage use in developing countries.This study will inform the safe deployment of DCT technologies needed for combatting pandemics in low-income countries.

ETHICS AND DISSEMINATION

This work has received ethics approval from the School of Public Health Higher Degrees, Research and Ethics Committee at Makerere University and The Uganda National Council for Science and Technology. This work will be disseminated through peer-reviewed publications, our websites https://project-thea.org/ and Github for the open source code https://github.com/project-thea/.

Complex model calibration through emulation, a worked example for a stochastic epidemic model

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.

Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus

Pathogens such as African swine fever virus (ASFV) are an increasing threat to global livestock production with implications for economic well-being and food security. Quantification of epidemiological parameters, such as transmission rates and latent and infectious periods, is critical to inform efficient disease control. Parameter estimation for livestock disease systems is often reliant upon transmission experiments, which provide valuable insights in the epidemiology of disease but which may also be unrepresentative of at-risk populations and incur economic and animal welfare costs. Routinely collected mortality data are a potential source of readily available and representative information regarding disease transmission early in outbreaks. We develop methodology to conduct exact Bayesian parameter inference from mortality data using reversible jump Markov chain Monte Carlo incorporating multiple routes of transmission (e.g. within-farm secondary and background transmission from external sources). We use this methodology to infer epidemiological parameters for ASFV using data from outbreaks on nine farms in the Russian Federation. This approach improves inference on transmission rates in comparison with previous methods based on approximate Bayesian computation, allows better estimation of time of introduction and could readily be applied to other outbreaks or pathogens.

High Prevalence and Factors Associated With the Distribution of the Integron intI1 and intI2 Genes in Scottish Cattle Herds

Integrons are genetic elements that capture and express antimicrobial resistance genes within arrays, facilitating horizontal spread of multiple drug resistance in a range of bacterial species. The aim of this study was to estimate prevalence for class 1, 2, and 3 integrons in Scottish cattle and examine whether spatial, seasonal or herd management factors influenced integron herd status. We used fecal samples collected from 108 Scottish cattle herds in a national, cross-sectional survey between 2014 and 2015, and screened fecal DNA extracts by multiplex PCR for the integrase genes intI1, intI2, and intI3. Herd-level prevalence was estimated [95% confidence interval (CI)] for intI1 as 76.9% (67.8-84.0%) and intI2 as 82.4% (73.9-88.6%). We did not detect intI3 in any of the herd samples tested. A regional effect was observed for intI1, highest in the North East (OR 11.5, 95% CI: 1.0-130.9, P = 0.05) and South East (OR 8.7, 95% CI: 1.1-20.9, P = 0.04), lowest in the Highlands. A generalized linear mixed model was used to test for potential associations between herd status and cattle management, soil type and regional livestock density variables. Within the final multivariable model, factors associated with herd positivity for intI1 included spring season of the year (OR 6.3, 95% CI: 1.1-36.4, P = 0.04) and watering cattle from a natural spring source (OR 4.4, 95% CI: 1.3-14.8, P = 0.017), and cattle being housed at the time of sampling for intI2 (OR 75.0, 95% CI: 10.4-540.5, P < 0.001). This study provides baseline estimates for integron prevalence in Scottish cattle and identifies factors that may be associated with carriage that warrant future investigation.

Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain

In the United Kingdom, despite decades of control efforts, bovine tuberculosis (bTB) has not been controlled and currently costs ~ £100 m annually. Critical in the failure of control efforts has been the lack of a sufficiently sensitive diagnostic test. Here we use machine learning (ML) to predict herd-level bTB breakdowns in Great Britain (GB) with the aim of improving herd-level diagnostic sensitivity. The results of routinely-collected herd-level tests were correlated with risk factor data. Four ML methods were independently trained with data from 2012–2014 including ~ 4700 positive herd-level test results annually. The best model’s performance was compared to the observed sensitivity and specificity of the herd-level test calculated on the 2015 data resulting in an increased herd-level sensitivity from 61.3 to 67.6% (95% confidence interval (CI): 66.4–68.8%) and herd-level specificity from 90.5 to 92.3% (95% CI: 91.6–93.1%). This approach can improve predictive capability for herd-level bTB and support disease control.

Post-outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa's controlled area

An African horse sickness (AHS) outbreak occurred in March and April 2016 in the controlled area of South Africa. This extended an existing trade suspension of live equids from South Africa to the European Union. In the post-outbreak period ongoing passive and active surveillance, the latter in the form of monthly sentinel surveillance and a stand-alone freedom from disease survey in March 2017, took place. We describe a stochastic scenario tree analysis of these surveillance components for 24 months, starting July 2016, in three distinct geographic areas of the controlled area. Given that AHS was not detected, the probability of being free from AHS was between 98.3% and 99.8% assuming that, if it were present, it would have a prevalence of at least one infected animal in 1% of herds. This high level of freedom probability had been attained in all three areas within the first 9 months of the 2-year period. The primary driver of surveillance outcomes was the passive surveillance component. Active surveillance components contributed minimally (<0.2%) to the final probability of freedom. Sensitivity analysis showed that the probability of infected horses showing clinical signs was an important parameter influencing the system surveillance sensitivity. The monthly probability of disease introduction needed to be increased to 20% and greater to decrease the overall probability of freedom to below 90%. Current global standards require a 2-year post-incursion period of AHS freedom before re-evaluation of free zone status. Our findings show that the length of this period could be decreased if adequately sensitive surveillance is performed. In order to comply with international standards, active surveillance will remain a component of AHS surveillance in South Africa. Passive surveillance, however, can provide substantial evidence supporting AHS freedom status declarations, and further investment in this surveillance activity would be beneficial.

Uptake of Diagnostic Tests by Livestock Farmers: A Stochastic Game Theory Approach

Game theory examines strategic decision-making in situations of conflict, cooperation, and coordination. It has become an established tool in economics, psychology and political science, and more recently has been applied to disease control. Used to examine vaccination uptake in human medicine, game theory shows that when vaccination is voluntary some individuals will choose to "free-ride" on the protection provided by others, resulting in insufficient coverage for control of a vaccine-preventable disease. Here, we use game theory to examine farmer uptake of a new diagnostic ELISA test for sheep scab-a highly infectious disease with an estimated cost exceeding £8M per year to the UK industry. The stochastic game models decisions made by neighboring farmers when deciding whether to adopt the newly available test, which can detect subclinical infestation. A key element of the stochastic game framework is that it allows multiple states. Depending on infestation status and test adoption decisions in the previous year, a farm may be at high, medium or low risk of infestation this year-a status which influences the decision the farmer makes and the farmer payoffs. Ultimately, each farmer's decision depends on the costs of using the diagnostic test vs. the benefits of enhanced disease control, which may only accrue in the longer term. The extent to which a farmer values short-term over long-term benefits reflects external factors such as inflation or individual characteristics such as patience. Our results show that when using realistic parameters and with a test cost around 50% more than the current clinical diagnosis, the test will be adopted in the high-risk state, but not in the low-risk state. For the medium risk state, test adoption will depend on whether the farmer takes a long-term or short-term view. We show that these outcomes are relatively robust to change in test costs and, moreover, that whilst the farmers adopting the test would not expect to see large gains in profitability, substantial reduction in sheep scab (and associated welfare implications) could be achieved in a cost-neutral way to the industry.

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

When assessing the role of live animal trade networks in the spread of infectious diseases in livestock, attention has focused mainly on direct movements of animals between premises, whereas the role of haulage vehicles used during transport, an indirect route for disease transmission, has largely been ignored. Here, we have assessed the impact of sharing haulage vehicles from livestock transport service providers on the connectivity between farms as well as on the spread of swine infectious diseases in Great Britain (GB). Using all pig movement records between April 2012 and March 2014 in GB, we built a series of directed and weighted static multiplex networks consisting of two layers of identical nodes, where nodes (farms) are linked either by (a) the direct movement of pigs and (b) the shared use of haulage vehicles. The haulage contact definition integrates the date of the move and the duration Δ s that lorries are left contaminated by pathogens, hence accounting for the temporal aspect of contact events. For increasing Δ s , descriptive network analyses were performed to assess the role of haulage on network connectivity. We then explored how viruses may spread throughout the GB pig sector by computing the reproduction number R . Our results showed that sharing haulage vehicles increases the number of contacts between farms by >50% and represents an important driver of disease transmission. In particular, sharing haulage vehicles, even if Δ s  < 1 day, will limit the benefit of the standstill regulation, increase the number of premises that could be infected in an outbreak, and more easily raise R above 1. This work confirms that sharing haulage vehicles has significant potential for spreading infectious diseases within the pig sector. The cleansing and disinfection process of haulage vehicles is therefore a critical control point for disease transmission risk mitigation.

Endemic foot and mouth disease: pastoral in-herd disease dynamics in sub-Saharan Africa

Foot and mouth disease (FMD) burden disproportionally affects Africa where it is considered endemic. Smallholder livestock keepers experience significant losses due to disease, but the dynamics and mechanisms underlying persistence at the herd-level and beyond remain poorly understood. We address this knowledge gap using stochastic, compartmental modelling to explore FMD virus (FMDV) persistence, outbreak dynamics and disease burden in individual cattle herds within an endemic setting. Our analysis suggests repeated introduction of virus from outside the herd is required for long-term viral persistence, irrespective of carrier presence. Risk of new disease exposures resulting in significant secondary outbreaks is reduced by the presence of immune individuals giving rise to a period of reduced risk, the predicted duration of which suggests that multiple strains of FMDV are responsible for observed yearly herd-level outbreaks. Our analysis suggests management of population turnover could potentially reduce disease burden and deliberate infection of cattle, practiced by local livestock keepers in parts of Africa, has little effect on the duration of the reduced risk period but increases disease burden. This work suggests that FMD control should be implemented beyond individual herds but, in the interim, herd management may be used to reduced FMD impact to livestock keepers.

Assessing the potential of plains zebra to maintain African horse sickness in the Western Cape Province, South Africa

African horse sickness (AHS) is a disease of equids that results in a non-tariff barrier to the trade of live equids from affected countries. AHS is endemic in South Africa except for a controlled area in the Western Cape Province (WCP) where sporadic outbreaks have occurred in the past 2 decades. There is potential that the presence of zebra populations, thought to be the natural reservoir hosts for AHS, in the WCP could maintain AHS virus circulation in the area and act as a year-round source of infection for horses. However, it remains unclear whether the epidemiology or the ecological conditions present in the WCP would enable persistent circulation of AHS in the local zebra populations. Here we developed a hybrid deterministic-stochastic vector-host compartmental model of AHS transmission in plains zebra (Equus quagga), where host populations are age- and sex-structured and for which population and AHS transmission dynamics are modulated by rainfall and temperature conditions. Using this model, we showed that populations of plains zebra present in the WCP are not sufficiently large for AHS introduction events to become endemic and that coastal populations of zebra need to be >2500 individuals for AHS to persist >2 years, even if zebras are infectious for more than 50 days. AHS cannot become endemic in the coastal population of the WCP unless the zebra population involves at least 50,000 individuals. Finally, inland populations of plains zebra in the WCP may represent a risk for AHS to persist but would require populations of at least 500 zebras or show unrealistic duration of infectiousness for AHS introduction events to become endemic. Our results provide evidence that the risk of AHS persistence from a single introduction event in a given plains zebra population in the WCP is extremely low and it is unlikely to represent a long-term source of infection for local horses.

Framework for Estimating Indirect Costs in Animal Health Using Time Series Analysis

Traditionally, cost-benefit analyses (CBAs) focus on the direct costs of animal disease, including animal mortality, morbidity, and associated response costs. However, such approaches often fail to capture the wider, dynamic market impacts that could arise. The duration of these market dislocations could last well after an initial disease outbreak. More generally, current approaches also muddle definitions of indirect costs, confusing debate on the scope of the totalities of disease-induced economic impacts. The aim of this work was to clarify definitions of indirect costs in the context of animal diseases and to apply this definition to a time series methodological framework to estimate the indirect costs of animal disease control strategies, using a foot and mouth disease (FMD) outbreak in Scotland as a case study. Time series analysis is an econometric method for analyzing statistical relationships between data series over time, thus allowing insights into how market dynamics may change following a disease outbreak. First an epidemiological model simulated FMD disease dynamics based on alternative control strategies. Output from the epidemiological model was used to quantify direct costs and applied in a multivariate vector error correction model to quantify the indirect costs of alternative vaccine stock strategies as a result of FMD. Indirect costs were defined as the economic losses incurred in markets after disease freedom is declared. As such, our definition of indirect costs captures the knock-on price and quantity effects in six agricultural markets after a disease outbreak. Our results suggest that controlling a FMD epidemic with vaccination is less costly in direct and indirect costs relative to a no vaccination (i.e., "cull only") strategy, when considering large FMD outbreaks in Scotland. Our research clarifies and provides a framework for estimating indirect costs, which is applicable to both exotic and endemic diseases. Standard accounting CBAs only capture activities in isolation, ignore linkages across sectors, and do not consider price effects. However, our framework not only delineates when indirect costs start, but also captures the wider knock-on price effects between sectors, which are often omitted from CBAs but are necessary to support decision-making in animal disease prevention and control strategies.

Characterizing Livestock Markets, Primary Diseases, and Key Management Practices Along the Livestock Supply Chain in Cameroon

Live animal markets are common hotspots for the dispersal of multiple infectious diseases in various production systems globally. In Cameroon livestock trade occurs predominantly via a system of livestock markets. Improving the understanding of the risks associated with livestock trade systems and markets is, therefore, key to design targeted and evidence-based interventions. In the current study, official transaction records for a 12-month period were collected from 62 livestock markets across Central and Southern Cameroon, in combination with a questionnaire-based survey with the livestock markets stakeholders. The available information collected at these markets was used to characterize their structural and functional organization. Based on trade volume, cattle price and the intensity of stakeholder attendance, four main classes of livestock markets were identified. Despite an evident hierarchical structure of the system, a relatively limited pool of infectious diseases was consistently reported as predominant across market classes, highlighting homogeneous disease risks along the livestock supply chain. Conversely, the variable livestock management practices reported (e.g., traded species, husbandry practices, and transhumance habits) highlighted diverse potential risks for disease dissemination among market classes. Making use of readily available commercial information at livestock markets, this study describes a rapid approach for market characterization and classification. Simultaneously, this study identifies primary diseases and management practices at risk and provides the opportunity to inform evidence-based and strategic communication, surveillance and control approaches aiming at mitigating these risks for diseases dissemination through the livestock supply chain in Cameroon.

Cattle transhumance and agropastoral nomadic herding practices in Central Cameroon

BACKGROUND

In sub-Saharan Africa, livestock transhumance represents a key adaptation strategy to environmental variability. In this context, seasonal livestock transhumance also plays an important role in driving the dynamics of multiple livestock infectious diseases. In Cameroon, cattle transhumance is a common practice during the dry season across all the main livestock production zones. Currently, the little recorded information of the migratory routes, grazing locations and nomadic herding practices adopted by pastoralists, limits our understanding of pastoral cattle movements in the country. GPS-tracking technology in combination with a questionnaire based-survey were used to study a limited pool of 10 cattle herds from the Adamawa Region of Cameroon during their seasonal migration, between October 2014 and May 2015. The data were used to analyse the trajectories and movement patterns, and to characterize the key animal health aspects related to this seasonal migration in Cameroon.

RESULTS

Several administrative Regions of the country were visited by the transhumant herds over more than 6 months. Herds travelled between 53 and 170 km to their transhumance grazing areas adopting different strategies, some travelling directly to their destination areas while others having multiple resting periods and grazing areas. Despite their limitations, these are among the first detailed data available on transhumance in Cameroon. These reports highlight key livestock health issues and the potential for multiple types of interactions between transhumant herds and other domestic and wild animals, as well as with the formal livestock trading system.

CONCLUSION

Overall, these findings provide useful insights into transhumance patterns and into the related animal health implications recorded in Cameroon. This knowledge could better inform evidence-based approaches for designing infectious diseases surveillance and control measures and help driving further studies to improve the understanding of risks associated with livestock movements in the region.

Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Mortality data are routinely collected for many livestock and poultry species, and they are often used for epidemiological purposes, including estimating transmission parameters. In this study, we infer transmission rates for African swine fever virus (ASFV), an important transboundary disease of swine, using mortality data collected from nine pig herds in the Russian Federation with confirmed outbreaks of ASFV. Parameters in a stochastic model for the transmission of ASFV within a herd were estimated using approximate Bayesian computation. Estimates for the basic reproduction number varied amongst herds, ranging from 4.4 to 17.3. This was primarily a consequence of differences in transmission rate (range: 0.7-2.2), but also differences in the mean infectious period (range: 4.5-8.3 days). We also found differences amongst herds in the mean latent period (range: 5.8-9.7 days). Furthermore, our results suggest that ASFV could be circulating in a herd for several weeks before a substantial increase in mortality is observed in a herd, limiting the usefulness of mortality data as a means of early detection of an outbreak. However, our results also show that mortality data are a potential source of data from which to infer transmission parameters, at least for diseases which cause high mortality.

Assessing the Economic Impact of Vaccine Availability When Controlling Foot and Mouth Disease Outbreaks

Predictive models have been used extensively to assess the likely effectiveness of vaccination policies as part of control measures in the event of a foot and mouth disease (FMD) outbreak. However, the availability of vaccine stocks and the impact of vaccine availability on disease control strategies represent a key uncertainty when assessing potential control strategies. Using an epidemiological, spatially explicit, simulation model in combination with a direct cost calculator, we assessed how vaccine availability constraints may affect the economic benefit of a “vaccination-to-live” strategy during a FMD outbreak in Scotland, when implemented alongside culling of infected premises and dangerous contacts. We investigated the impact of vaccine stock size and restocking delays on epidemiological and economic outcomes. We also assessed delays in the initial decision to vaccinate, maximum daily vaccination capacity, and vaccine efficacy. For scenarios with conditions conducive to large outbreaks, all vaccination strategies perform better than the strategy where only culling is implemented. A stock of 200,000 doses, enough to vaccinate 12% of the Scottish cattle population, would be sufficient to maximize the relative benefits of vaccination, both epidemiologically and economically. However, this generates a wider variation in economic cost than if vaccination is not implemented, making outcomes harder to predict. The probability of direct costs exceeding £500 million is reduced when vaccination is used and is steadily reduced further as the size of initial vaccine stock increases. If only a suboptimal quantity of vaccine doses is initially available (100,000 doses), restocking delays of more than 2 weeks rapidly increase the cost of controlling outbreaks. Impacts of low vaccine availability or restocking delays are particularly aggravated by delays in the initial decision to vaccinate, or low vaccine efficacy. Our findings confirm that implementing an emergency vaccination-to-live strategy in addition to the conventional stamping out strategy is economically beneficial in scenarios with conditions conducive to large FMD outbreaks in Scotland. However, the size of the initial vaccine stock available at the start of the outbreak and the interplay with other factors, such as vaccine efficacy and delays in restocking or implementing vaccination, should be considered in making decisions about optimal control strategies for FMD outbreaks.

Drivers of Live Cattle Price in the Livestock Trading System of Central Cameroon

Livestock production and trade are critical for the food security and welfare of rural households in sub-Saharan Africa. In Cameroon, animal trade consists mainly of live cattle commercialized through livestock markets. Identifying the factors contributing to cattle price formation is critical for designing effective policies for sustainable production and for increasing food availability. In this study, we evaluated the influence of a range of individual- and market-level factors on the price of cattle that were sold in all transactions (n = 118,017) recorded over a 12-month period from 31 livestock markets in the main cattle production area of the country. An information-theoretic approach using a generalized additive mixed-effect model was implemented to select the best explanatory model as well as evaluate the robustness of the identified drivers and the predictive ability of the model. The age and gender of the cattle traded were consistently found to be important drivers of the price (p < 0.01). Also, strong, but complex, relationships were found between cattle prices and both local human and bovine population densities. Finally, the model highlighted a positive association between the number of incoming trading connections of a livestock market and the price of the traded live cattle (p < 0.01). Although our analysis did not account for factors informing on specific phenotypic traits nor breed characteristics of cattle traded, nearly 50% of the observed variation in live cattle prices was explained by the final model. Ultimately, our model gives a large scale overview of drivers of cattle price formation in Cameroon and to our knowledge is the first study of this scale in Central Africa. Our findings represent an important milestone in designing efficient and sustainable animal health management programme in Cameroon and ensure livelihood sustainability for rural households.

Data-Driven Risk Assessment from Small Scale Epidemics: Estimation and Model Choice for Spatio-Temporal Data with Application to a Classical Swine Fever Outbreak

Livestock epidemics have the potential to give rise to significant economic, welfare, and social costs. Incursions of emerging and re-emerging pathogens may lead to small and repeated outbreaks. Analysis of the resulting data is statistically challenging but can inform disease preparedness reducing potential future losses. We present a framework for spatial risk assessment of disease incursions based on data from small localized historic outbreaks. We focus on between-farm spread of livestock pathogens and illustrate our methods by application to data on the small outbreak of Classical Swine Fever (CSF) that occurred in 2000 in East Anglia, UK. We apply models based on continuous time semi-Markov processes, using data-augmentation Markov Chain Monte Carlo techniques within a Bayesian framework to infer disease dynamics and detection from incompletely observed outbreaks. The spatial transmission kernel describing pathogen spread between farms, and the distribution of times between infection and detection, is estimated alongside unobserved exposure times. Our results demonstrate inference is reliable even for relatively small outbreaks when the data-generating model is known. However, associated risk assessments depend strongly on the form of the fitted transmission kernel. Therefore, for real applications, methods are needed to select the most appropriate model in light of the data. We assess standard Deviance Information Criteria (DIC) model selection tools and recently introduced latent residual methods of model assessment, in selecting the functional form of the spatial transmission kernel. These methods are applied to the CSF data, and tested in simulated scenarios which represent field data, but assume the data generation mechanism is known. Analysis of simulated scenarios shows that latent residual methods enable reliable selection of the transmission kernel even for small outbreaks whereas the DIC is less reliable. Moreover, compared with DIC, model choice based on latent residual assessment correlated better with predicted risk.

Vulnerability of the British swine industry to classical swine fever

Classical swine fever (CSF) is a notifiable, highly contagious viral disease of swine which results in severe welfare and economic consequences in affected countries. To improve preparedness, it is critical to have some understanding of how CSF would spread should it be introduced. Based on the data recorded during the 2000 epidemic of CSF in Great Britain (GB), a spatially explicit, premises-based model was developed to explore the risk of CSF spread in GB. We found that large outbreaks of CSF would be rare and generated from a limited number of areas in GB. Despite the consistently low vulnerability of the British swine industry to large CSF outbreaks, we identified concerns with respect to the role played by the non-commercial sector of the industry. The model further revealed how various epidemiological features may influence the spread of CSF in GB, highlighting the importance of between-farm biosecurity in preventing widespread dissemination of the virus. Knowledge of factors affecting the risk of spread are key components for surveillance planning and resource allocation, and this work provides a valuable stepping stone in guiding policy on CSF surveillance and control in GB.

The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia

BACKGROUND

Dengue is a major mosquito-borne viral disease and an important public health problem. Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures. In South-East Asia, half of all reported fatal infections are recorded in Indonesia, yet little is known about the epidemiology of dengue in this country.

METHODOLOGY/PRINCIPAL FINDINGS

Hospital-reported dengue cases in Banyumas regency, Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic, demographic and socio-economic factors on the risk of dengue infection. A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency. Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power.

CONCLUSIONS/SIGNIFICANCE

Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors (employment type, economic status). Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community. Although our analysis did not account for factors such as variations in immunity which need further investigation, this study can advise preventive measures in areas with similar patterns of reported dengue cases and environment.

Using national movement databases to help inform responses to swine disease outbreaks in Scotland: the impact of uncertainty around incursion time

Modelling is an important component of contingency planning and control of disease outbreaks. Dynamic network models are considered more useful than static models because they capture important dynamic patterns of farm behaviour as evidenced through animal movements. This study evaluates the usefulness of a dynamic network model of swine fever to predict pre-detection spread via movements of pigs, when there may be considerable uncertainty surrounding the time of incursion of infection. It explores the utility and limitations of animal movement data to inform such models and as such, provides some insight into the impact of improving traceability through real-time animal movement reporting and the use of electronic animal movement databases. The study concludes that the type of premises and uncertainty of the time of disease incursion will affect model accuracy and highlights the need for improvements in these areas.

Small-scale pig farmers' behavior, silent release of African swine fever virus and consequences for disease spread

The expanding distribution of African swine fever (ASF) is threatening the pig industry worldwide. Most outbreaks occur in backyard and small-scale herds, where poor farmers often attempt to limit the disease's economic consequences by the emergency sale of their pigs. The risk of African swine fever virus (ASFV) release via this emergency sale was investigated. Simulation modeling was used to study ASFV transmission in backyard and small-scale farms as well as the emergency sale of pigs, and the potential impact of improving farmers and traders' clinical diagnosis ability-its timeliness and/or accuracy-was assessed. The risk of ASFV release was shown to be high, and improving farmers' clinical diagnosis ability does not appear sufficient to effectively reduce this risk. Estimates obtained also showed that the distribution of herd size within the backyard and small-scale sectors influences the relative contribution of these farms to the risk of release of infected pigs. These findings can inform surveillance and control programs.

Use of a mathematical model to study the dynamics ofCtenocephalides felispopulations in the home environment and the impact of various control measures

The biology of fleas has been studied by a number of authors, as has the impact of various types of control measures. However, there are no mathematical models simulating the dynamics of a population of Ctenocephalides felis felis fleas on their host (the cat) and in their close environment (apartment). The model presented in this paper allows for integration of the numerous biological and behavioural parameters of the parasites and their hosts and for the variation of these same parameters. The various types of control measures can be programmed so that their impact over time can be studied. The model confirms the key role played by adult fleas, or emerged fleas contained in the cocoon. Only regular applications of persistent insecticides to the host animal will enable control of the parasite population. A combination of these insecticides with an IGR (Insect Growth Regulator) will accelerate decontamination of the home environment and see the disappearance of the parasites altogether if they are not reintroduced. The association of additional measures such as vacuum cleaning will accelerate the process of decontamination but will have no impact if carried out in isolation. One-off treatment with insecticide will not enable a reduction in the parasite population, even if carried out frequently. Use of insecticides on the home environment premises alone does not appear to be an adequate means of control. The present model can be used to test various integrated control measures which take into account different factors such as the number of host animals, the frequency of movement outdoors, the impact of the seasons.

How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis

BACKGROUND

The impact of non-commercial producers on disease spread via livestock movement is related to their level of interaction with other commercial actors within the industry. Although understanding these relationships is crucial in order to identify likely routes of disease incursion and transmission prior to disease detection, there has been little research in this area due to the difficulties of capturing movements of small producers with sufficient resolution. Here, we used the Scottish Livestock Electronic Identification and Traceability (ScotEID) database to describe the movement patterns of different pig production systems which may affect the risk of disease spread within the swine industry. In particular, we focused on the role of small pig producers.

RESULTS

Between January 2012 and May 2013, 23,169 batches of pigs were recorded moving animals between 2382 known unique premises. Although the majority of movements (61%) were to a slaughterhouse, the non-commercial and the commercial sectors of the Scottish swine industry coexist, with on- and off-movement of animals occurring relatively frequently. For instance, 13% and 4% of non-slaughter movements from professional producers were sent to a non-assured commercial producer or to a small producer, respectively; whereas 43% and 22% of movements from non-assured commercial farms were sent to a professional or a small producer, respectively. We further identified differences between producer types in several animal movement characteristics which are known to increase the risk of disease spread. Particularly, the distance travelled and the use of haulage were found to be significantly different between producers.

CONCLUSIONS

These results showed that commercial producers are not isolated from the non-commercial sector of the Scottish swine industry and may frequently interact, either directly or indirectly. The observed patterns in the frequency of movements, the type of producers involved, the distance travelled and the use of haulage companies provide insights into the structure of the Scottish swine industry, but also highlight different features that may increase the risk of infectious diseases spread in both Scotland and the UK. Such knowledge is critical for developing more robust biosecurity and surveillance plans and better preparing Scotland against incursions of emerging swine diseases.

E. coli O157 on Scottish cattle farms: evidence of local spread and persistence using repeat cross-sectional data

Background

Escherichia coli (E. coli) O157 is a virulent zoonotic strain of enterohaemorrhagic E. coli. In Scotland (1998-2008) the annual reported rate of human infection is 4.4 per 100,000 population which is consistently higher than other regions of the UK and abroad. Cattle are the primary reservoir. Thus understanding infection dynamics in cattle is paramount to reducing human infections.

A large database was created for farms sampled in two cross-sectional surveys carried out in Scotland (1998 - 2004). A statistical model was generated to identify risk factors for the presence of E. coli O157 on farms. Specific hypotheses were tested regarding the presence of E. coli O157 on local farms and the farms previous status. Pulsed-field gel electrophoresis (PFGE) profiles were further examined to ascertain whether local spread or persistence of strains could be inferred.

Results

The presence of an E. coli O157 positive local farm (average distance: 5.96km) in the Highlands, North East and South West, farm size and the number of cattle moved onto the farm 8 weeks prior to sampling were significant risk factors for the presence of E. coli O157 on farms. Previous status of a farm was not a significant predictor of current status (p = 0.398). Farms within the same sampling cluster were significantly more likely to be the same PFGE type (p < 0.001), implicating spread of strains between local farms. Isolates with identical PFGE types were observed to persist across the two surveys, including 3 that were identified on the same farm, suggesting an environmental reservoir. PFGE types that were persistent were more likely to have been observed in human clinical infections in Scotland (p < 0.001) from the same time frame.

Conclusions

The results of this study demonstrate the spread of E. coli O157 between local farms and highlight the potential link between persistent cattle strains and human clinical infections in Scotland. This novel insight into the epidemiology of Scottish E. coli O157 paves the way for future research into the mechanisms of transmission which should help with the design of control measures to reduce E. coli O157 from livestock-related sources.

The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling

BACKGROUND

When modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on the distance between point locations of farms. Indeed, underlying the spatial pattern of spread are the contact networks involved in transmission. Accordingly, area-weighted tessellation around farm point locations has been used to approximate field-contiguity and simulate the effect of contiguous premises (CP) culling for FMD. Here, geographic data were used to determine contiguity based on distance between premises' fields and presence of landscape features for two sample areas in Scotland. Sensitivity, positive predictive value, and the True Skill Statistic (TSS) were calculated to determine how point distance measures and area-weighted tessellation compared to the 'gold standard' of the map-based measures in identifying CPs. In addition, the mean degree and density of the different contact networks were calculated.

RESULTS

Utilising point distances <1 km and <5 km as a measure for contiguity resulted in poor discrimination between map-based CPs/non-CPs (TSS 0.279-0.344 and 0.385-0.400, respectively). Point distance <1 km missed a high proportion of map-based CPs; <5 km point distance picked up a high proportion of map-based non-CPs as CPs. Area-weighted tessellation performed best, with reasonable discrimination between map-based CPs/non-CPs (TSS 0.617-0.737) and comparable mean degree and density. Landscape features altered network properties considerably when taken into account.

CONCLUSION

The farming landscape is not homogeneous. Basing contiguity on geographic locations of field boundaries and including landscape features known to affect transmission into FMD models are likely to improve individual farm-level accuracy of spatial predictions in the event of future outbreaks. If a substantial proportion of FMD transmission events are by contiguous spread, and CPs should be assigned an elevated relative transmission rate, the shape of the kernel could be significantly altered since ability to discriminate between map-based CPs and non-CPs is different over different Euclidean distances.

Vaccination against foot-and-mouth disease: do initial conditions affect its benefit?

When facing incursion of a major livestock infectious disease, the decision to implement a vaccination programme is made at the national level. To make this decision, governments must consider whether the benefits of vaccination are sufficient to outweigh potential additional costs, including further trade restrictions that may be imposed due to the implementation of vaccination. However, little consensus exists on the factors triggering its implementation on the field. This work explores the effect of several triggers in the implementation of a reactive vaccination-to-live policy when facing epidemics of foot-and-mouth disease. In particular, we tested whether changes in the location of the incursion and the delay of implementation would affect the epidemiological benefit of such a policy in the context of Scotland. To reach this goal, we used a spatial, premises-based model that has been extensively used to investigate the effectiveness of mitigation procedures in Great Britain. The results show that the decision to vaccinate, or not, is not straightforward and strongly depends on the underlying local structure of the population-at-risk. With regards to disease incursion preparedness, simply identifying areas of highest population density may not capture all complexities that may influence the spread of disease as well as the benefit of implementing vaccination. However, if a decision to vaccinate is made, we show that delaying its implementation in the field may markedly reduce its benefit. This work provides guidelines to support policy makers in their decision to implement, or not, a vaccination-to-live policy when facing epidemics of infectious livestock disease.

Spatial prediction of brushtail possum (Trichosurus vulpecula) distribution using a combination of remotely sensed and field-observed environmental data

Context In New Zealand, the introduced brushtail possum, Trichosurus vulpecula, is a reservoir of bovine tuberculosis and as such poses a major threat to the livestock industry. Aerial 1080 poisoning is an important tool for possum control but is expensive, creating an ongoing need for ever more cost-effective ways of using this technique. Aims To develop geographic information system (GIS) models to better predict spatial variation in the distribution of unmanaged possum populations, to facilitate better targeting of control activities. Methods Relative abundance of possums and their distribution among habitat types were surveyed in a dry high-country area of the northern South Island. Two GIS-based models were developed to predict the relative abundance of possums on trap lines. The first model used remotely sensed (digital) environmental data; the second complemented the remotely sensed data with fine-scale habitat and topographic data collected on the ground. Key results Digital environmental factors and habitat features proved to be key predictors of relative possum abundance. In both GIS models, height above valley floor, presence of forest cover and mean annual temperature were the strongest predictors. Conclusions Predictive maps (projections) of relative possum abundance produced from these models can provide useful decision-support tools for pest-control managers, by enabling possum control to be targeted spatially. Implications Spatially targeted pest control could allow effective control activities for invasive species or disease vectors to be applied at a lower cost for the same benefit.

A metapopulation model to assess the capacity of spread of meticillin-resistant Staphylococcus aureus ST398 in humans

The emergence of the livestock-associated clone of meticillin-resistant Staphylococcus aureus (MRSA) ST398 is a serious public health issue throughout Europe. In The Netherlands a stringent 'search-and-destroy' policy has been adopted, keeping low the level of MRSA prevalence. However, reports have recently emerged of transmission events between humans showing no links to livestock, contradicting belief that MRSA ST398 is poorly transmissible in humans. The question regarding the transmissibility of MRSA ST398 in humans therefore remains of great interest. Here, we investigated the capacity of MRSA ST398 to spread into an entirely susceptible human population subject to the effect of a single MRSA-positive commercial pig farm. Using a stochastic, discrete-time metapopulation model, we explored the effect of varying both the probability of persistent carriage and that of acquiring MRSA due to contact with pigs on the transmission dynamics of MRSA ST398 in humans. In particular, we assessed the value and key determinants of the basic reproduction ratio (R(0)) for MRSA ST398. Simulations showed that the presence of recurrent exposures with pigs in risky populations allows MRSA ST398 to persist in the metapopulation and transmission events to occur beyond the farming community, even when the probability of persistent carriage is low. We further showed that persistent carriage should occur in less than 10% of the time for MRSA ST398 to conserve epidemiological characteristics similar to what has been previously reported. These results indicate that implementing control policy that only targets human carriers may not be sufficient to control MRSA ST398 in the community if it remains in pigs. We argue that farm-level control measures should be implemented if an eradication programme is to be considered.

Exploratory space-time analyses of Rift Valley Fever in South Africa in 2008-2011

BACKGROUND

Rift Valley fever (RVF) is a zoonotic arbovirosis for which the primary hosts are domestic livestock (cattle, sheep and goats). RVF was first described in South Africa in 1950-1951. Mechanisms for short and long distance transmission have been hypothesised, but there is little supporting evidence. Here we describe RVF occurrence and spatial distribution in South Africa in 2008-11, and investigate the presence of a contagious process in order to generate hypotheses on the different mechanisms of transmission.

METHODOLOGY/PRINCIPAL FINDINGS

A total of 658 cases were extracted from World Animal Health Information Database. Descriptive statistics, epidemic curves and maps were produced. The space-time K-function was used to test for evidence of space-time interaction. Five RVF outbreak waves (one in 2008, two in 2009, one in 2010 and one in 2011) of varying duration, location and size were reported. About 70% of cases (n = 471) occurred in 2010, when the epidemic was almost country-wide. No strong evidence of space-time interaction was found for 2008 or the second wave in 2009. In the first wave of 2009, a significant space-time interaction was detected for up to one month and over 40 km. In 2010 and 2011 a significant intense, short and localised space-time interaction (up to 3 days and 15 km) was detected, followed by one of lower intensity (up to 2 weeks and 35 to 90 km).

CONCLUSIONS/SIGNIFICANCE

The description of the spatiotemporal patterns of RVF in South Africa between 2008 and 2011 supports the hypothesis that during an epidemic, disease spread may be supported by factors other than active vector dispersal. Limitations of under-reporting and space-time K-function properties are discussed. Further spatial analyses and data are required to explain factors and mechanisms driving RVF spread.

Contact patterns as a risk factor for bovine tuberculosis infection in a free-living adult brushtail possum Trichosurus vulpecula population

The aim of this study was to identify risk factors for bovine tuberculosis (TB) in a free-roaming, capture-mark-recapture monitored possum Trichosurus vulpecula population in a 22-ha study site at Castlepoint, New Zealand from 1 April 1989 to 31 March 1994. A matched case-control design was used to evaluate the influence of sex, habitat and contact opportunities on TB risk. Cases comprised possums identified as TB-positive throughout the study period. Controls were selected from the group of possums that were captured and showed no clinical signs of TB throughout the study period. Measures derived from a social network analysis of possum capture locations such as degree, clustering coefficient (CC) and betweenness were used to represent potential contact opportunities among possums. Network analysis measures recorded for individual possums in the 12-month period before a diagnosis of TB were evaluated in a conditional logistic regression model. We found no evidence of an association between case status and the total number of possums with which there was potential contact (degree) (P=0.5). The odds of cases being exposed to unit increases in the number of TB-positive contacts was 2.50 (95% CI 1.24-5.05; P<0.01) times that of controls. This effect was conditional on the total number of potential contacts made, with a negative interaction with increasing degree. These findings indicate that potential contact with TB-positive possums increases the odds of disease whereas potential contact with large numbers of possums does not. This suggests that multiple contacts with TB-positive possum(s) are necessary for transmission of TB and this is more likely to occur in networks that are smaller. We challenge the hypothesis that contact with large numbers of individuals increases the probability of becoming TB infected and argue that individual contact behaviour is a determinant of the creation of TB foci within free-living possum populations.

Mapping brucellosis risk in communities in the Republic of Armenia

We describe the geographical patterns and identified factors associated with serological evidence of brucellosis in ruminants in Armenian communities during 2006 and 2007. The data comprised the two first complete years of the current national test-and-slaughter control programme for cattle, sheep and goats. Overall, 29% and 21% of the 858 communities involved in this study reported brucellosis in their respective cattle and small ruminant populations. The national brucellosis control data showed a widespread and uneven distribution of brucellosis throughout the Republic of Armenia for both cattle and small ruminants. The geographical areas of greater risk of communities having seropositive animals were different for cattle and small ruminant populations but most of the associated factors were similar. Several areas where the likelihood of disease occurrence was predicted poorly by the statistical models were also identified. These latter findings are indicative of either less than perfect testing and reporting procedures or unexplained epidemiological factors operating in those particular areas. The analyses provided valuable insights into understanding the brucellosis epidemiology at the community level which operates in small ruminant and cattle populations, and identified priority areas for implementing targeted risk-based surveillance and disease control interventions.

A descriptive spatial analysis of bovine tuberculosis in intensively controlled cattle farms in New Zealand

We describe the temporal and geographical distribution of confirmed cases of bovine tuberculosis (TB) in a population of cattle in the south-east of the North Island of New Zealand. Data were derived from routine TB testing conducted between 1980 and 2003 and included details for 69 farms. Four six-year periods were defined to coincide with changes in depopulation strategies against the wildlife TB reservoir, the brushtail possum Trichosurus vulpecula. For the periods 1980 to 1985 and 1986 to 1991 the median annual incidence rate of TB was 0.4 and 4.7 cases per 1000 cattle-years at risk, respectively. For the period 1992 to 2003 the median annual incidence rate of TB decreased to 1.8 cases per 1000 cattle-years at risk, coincident with the use of poisoning to control possums in the surrounding forest park (a major possum habitat area). We identified clusters of TB cases adjacent to the forest park and found no evidence of spatio-temporal interaction of TB risk among farms. Our findings support the hypothesis that possums living in the forest park are a source of bovine TB in this area and that farm-to-farm spread of disease was not an important infection mechanism.