Spatial and temporal epidemiological analysis in the Big Data era.

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

The value of information: Current challenges in surveillance implementation.

Animal health surveillance is a complex activity that involves multiple stakeholders and provides decision support across sectors. Despite progress in the design of surveillance systems, some technical challenges remain, specifically for emerging hazards. Surveillance can also be impacted by political interests and costly consequences of case reporting, particularly in relation to international trade. Constraints on surveillance can therefore be of technical, economic and political nature. From an economic perspective, both surveillance and intervention are resource-using activities that are part of a mitigation strategy. Surveillance provides information for intervention decisions and thereby helps to offset negative effects of animal disease and to reduce the decision uncertainty associated with choices on disease control. It thus creates monetary and non-monetary benefits, both of which may be challenging to quantify. The technical relationships between surveillance, intervention and loss avoidance have not been established for most hazards despite being important consideration for investment decisions. Therefore, surveillance cannot just be maximised to minimise intervention costs. Economic appraisals of surveillance need to be done on a case by case basis for any hazard considering both surveillance and intervention performance, the losses avoided and the values attached to them. This can be achieved by using an evaluation approach which provides a systematic investigation of the worth or merit of surveillance activities. Evaluation is driven by a specific evaluation question which for surveillance systems commonly considers effectiveness, efficiency, implementation and/or compliance issues. More work is needed to provide guidance on the appropriate selection of evaluation attributes and general good practice in surveillance evaluation. Due to technical challenges, economic constraints and variable levels of capacity, the implementation of surveillance systems remains variable. Political and legal issues are also influential. A particular challenge exists during outbreaks when surveillance needs to be conducted under emergency conditions. Decision support systems can help make epidemiologically and economically sound choices amongst surveillance options. However, contingency planning is advisable so that pre-defined options allow for rapid decision making.

Epidemiological research and evidence based medicine: How do they fit and for whom.

Evidence based medicine involves using the best current information to inform patient care. In veterinary medicine, evidence based veterinary medicine (EBVM) has been discussed for about 15 years. Epidemiology and EBVM are closely linked and epidemiologists can provide crucial support for the practice of EBVM. The secondary literature which summarizes important research into more accessible and applied work could benefit from additional involvement by epidemiologists. Epidemiologists have a broad range of stakeholders for their work and should consider who the specific audience is and what the important endpoints are for that audience. More work on reporting guidelines for observational studies and on issues relating to external validity are needed to facilitate EBVM. Epidemiologists should consider teaching veterinary, graduate and post-graduate students how to perform EBVM. Getting credit for efforts which support EBVM can be difficult but creative presentation of work, publications and grants relating to EBVM should help. Quite a few veterinary journals are actively soliciting manuscripts relating to EBVM.

Herd-level test performance based on uncertain estimates of individual test performance, individual true prevalence and herd true prevalence.

A generalized model was derived for understanding the performance of herd-testing protocols when there is uncertainty and variability in individual-level sensitivity, specificity, prevalence of infection within infected herds, and prevalence of infected herds in the population. The model uses Monte-Carlo techniques to provide estimates of test performance for a dichotomous classification of herd-disease status. Uncertainty and variability in input assumptions are described using empirical and parametric probability distributions. The model permits both cluster-correlated behavior of inputs and sampling of animals without replacement. Disease due to obligate parasites is modeled differently from that due to organisms that persist for long periods in the environment. Dependence among model outcomes is assessed using Spearman's rank correlation. Model output is suitable for inclusion in risk-assessment models requiring probabilistic estimates of herd-level test performance, such as those developed for food-safety decision making and import-export risk assessment. The model was demonstrated using an example scenario based on Shiga-like toxin (SLT) producing Escherichia coli O157 in Ontario beef-cattle herds. Inputs were derived from the literature and Statistics Canada agricultural census data. Where appropriate, these data were subjected to distribution-fitting techniques. Otherwise, subjective interpretation was used to select input distributions and their parameters. Simulation revealed that the distribution of herd-level sensitivity for detecting herds infected with SLT producing E. coli O157 has a large range (0.003-0.99) and a median of 0.19. Herd-level specificity also had a large range (0.58-1) and a median of 0.94. Distributions of herd-level positive and negative predictive values exhibited similar degrees of uncertainty. In combination with poor likelihood ratios for positive and negative herd tests, results indicate that the testing protocol investigated has limited ability to discriminate between herds infected and not infected with SLT producing E. coli O157.

An epidemiological and economic simulation model to evaluate the spread and control of infectious bovine rhinotracheitis in The Netherlands.

Bovine herpesvirus type I (BHV1), causing infectious bovine rhinotracheitis (IBR), was introduced in the Netherlands in 1971. In 1993, about 42% of the dairy cows had antibodies against BHV1. In the future, stricter requirements are anticipated regarding the health status of exported breeding cows and material. To support policymakers in their decisions on IBR eradication, a simulation model was developed in which the epidemiological and economic consequences of various control strategies were evaluated. This paper describes the model and provides an overview of some important outcomes. In the model, dairy herds were classified into different disease states based on (1) the reproduction ratio of the disease (R, defined as the number of secondary cases caused by one infectious animal) (2) the within-herd prevalence, within each value of R and (3) the expected number of infectious animals in an infectious herd within each prevalence range. The dynamic transition probability of a herd going from one state to another per week depends on direct contacts between animals, and other contacts such as transmission through fomites, indirect transmission through other species, airborne transmission and minor disease-specific routes such as venereal or iatrogenic transmission. Five control strategies, including both a voluntary vaccination program and a compulsory vaccination program for all dairy herds were evaluated. A voluntary vaccination program with 50% participation is not expected to lead to eradication of IBR. It appears that compulsory vaccination would be necessary to reach an IBR-free status.