EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance

Nöremark and Widgren BMC Veterinary Research 2014, 10:71 http://www.biomedcentral.com/1746-6148/10/71 SOFTWARE Open Access EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance Maria Nöremark* and Stefan Widgren Abstract Background: During outbreak of livestock diseases, contact tracing can be an important part of disease control. Animal movements can also be of relevance for risk-based surveillance and sampling, i.e. both when assessing consequences of introduction or likelihood of introduction. In many countries, animal movement data are collected with one of the major objectives to enable contact tracing. However, often an analytical step is needed to retrieve appropriate information for contact tracing or surveillance. Results: In this study, an open source tool was developed to structure livestock movement data to facilitate contact-tracing in real time during disease outbreaks and for input in risk-based surveillance and sampling. The tool, EpiContactTrace, was written in the R-language and uses the network parameters in-degree, out-degree, ingoing contact chain and outgoing contact chain (also called infection chain), which are relevant for forward and backward tracing respectively. The time-frames for backward and forward tracing can be specified independently and search can be done on one farm at a time or for all farms within the dataset. Different outputs are available; datasets with network measures, contacts visualised in a map and automatically generated reports for each farm either in HTML or PDF-format intended for the end-users, i.e. the veterinary authorities, regional disease control officers and field-veterinarians. EpiContactTrace is available as an R-package at the R-project website (http://cran.r-project.org/ web/packages/EpiContactTrace/). Conclusions: We believe this tool can help in disease control since it rapidly can structure essential contact information from large datasets. The reproducible reports make this tool robust and independent of manual compilation of data. The open source makes it accessible and easily adaptable for different needs. Keywords: Cattle-transport, Control strategies, Decision support systems, Epidemics, Eradication programs, Network analysis, GIS Background There are several reasons for preventing and controlling contagious diseases in livestock; securing food production, farmer economy, animal welfare and the zoonotic aspect. Both past and recent outbreaks have had large consequences both for the farming industry as well as other parts of the society [1,2]. Having tools ready to facilitate disease control and surveillance in critical stages of an outbreak can save time, aid in preventing further spread and thus minimise costs and consequences of the * Correspondence: Department of Disease Control and Epidemiology, SVA, National Veterinary Institute, 751 89 Uppsala, Sweden outbreak. Moreover, ongoing surveillance can contribute to early detection of disease outbreaks or assessing the disease status in a population. Applying a risk-based approach when sampling, i.e. searching in parts of the population where the likelihood of disease is higher or to identify strata where the consequences of disease introduction would be high, e.g. farms with many outgoing contacts can furthermore be a way to optimize surveillance resources [3,4]. Different diseases have different routes of spread. Yet, for most diseases, moving animals is considered to be one of the major risks for spreading disease between herds [5]. This is also one of the main reasons for registering transport of livestock in national databases, i.e. to © 2014 Nöremark and Widgren; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Nöremark and Widgren BMC Veterinary Research 2014, 10:71 http://www.biomedcentral.com/1746-6148/10/71 enable contact tracing in case of an outbreak [6]. However, the data are not always structured in such a way that information relevant for contact tracing or design of surveillance programmes can be easily accessed by the end user. In the following text the word ‘farm’ will be used, meaning not only the premises but also the livestock present on the farm. Contagious diseases often spread from farm to farm in a sequential way and in contact tracing, both backwards and forward tracing is important, i.e. identifying farms from which infected animals may have come, and identifying farms which may have received infected animals. The time window of possible introduction of infection to the herd is relevant when determining contacts of interest. Animals introduced after the possible window of introduction can be excluded as the source, and animals leaving the herd before the possible introduction will not have spread the disease. Although, the window cannot always be determined, knowledge about the incubation period in combination with first appearance of symptoms can guide in the right direction. This is illustrated in Figure 1. The sequential spread of diseases through live animal contacts has been described by Webb and Dubé and coworkers, through the network measure accessible world and infection chain [7,8]. Correspondingly, the possible source farms have been described using the ingoing infection chain [9]. In this article, we hereafter refer to these measures as outgoing contact chain and ingoing contact chain, since they measure contacts and not confirmed spread of infection. These two network measures take the temporal aspect of movements into account Page 2 of 9 and in combination with detailed information on the specific contacts identified, they are ideal for both backward and forward tracing of contacts through live animal movements during an outbreak (Figure 2). Moreover, the measures can be used to identify farms with many ingoing contacts or outgoing contacts, i.e. at high risk of introduction of disease or for spreading disease. In other words, information that could be relevant for risk-based surveillance and targeted sampling, or for targeted interventions during an outbreak. The information could also be of interest whenever animal movements are investigated as a risk factor for diseases occurrence. So far, many network articles published have been related to understanding structure of movements, modelling disease outbreaks, or to analyse movements post outbreak [10,11]. Although the effects of contact tracing on disease spread within a network has been investigated [12], there are fewer publications related to work providing applications for use during an ongoing outbreak [13]. However, the use of network measures for riskbased surveillance has been suggested by several authors [9,11,14,15] and also (...truncated)