Using data to fight disease

In today’s world, obtaining and sharing data gets easier every day. While this is positive in many ways, it has also led to issues over privacy and ethical usage. High quality data is essential to many areas of research, and fighting disease is no different.

EPIC researchers use data every day to analyse various aspects of disease, from the likelihood of incursion and possible patterns of spread, to potential success of control measures. In this article we outline some of the ways we use data to make our research more accurate, but also to help us build a bigger picture of disease outbreaks.

EPIC obtain data from a number of sources including Government and industry bodies. We have data sharing agreements in place with the organisations we share data with, and all data is received, stored and used within the scope of legal guidelines and regulations.

At a basic level, by looking at recorded holdings and animals, the EPIC team can map areas of the country more at risk of particular diseases due to high numbers of a specific species. We know the areas where populations are the densest, and we can apply our knowledge of disease sources and vectors to highlight high risk geographies.

One of the key ways our team use data, is to map animal movements. By analysing this information, we can see where there are strong links between locations. This information is very important in the case of a disease outbreak. We can track where animals are coming into Scotland, which sales or shows they are attending, and which abattoirs they are sent to for slaughter, building a picture of possible routes of disease incursion and spread. This also allows us to map any cross-over between species. This information is particularly important for a condition such as Foot & Mouth Disease, which affects multiple species. Knowing where these animals are likely to interact, can assist in projecting disease spread between species.

Understanding animal movements gives our team the ability to accurately predict future movements of animals, which allows them to build models that can be used to predict spread of disease. We can look at high risk potential sources of disease, and map the spread based on different sources, species and other key factors. Using historical disease data for comparison allows us to verify results, and we are fortunate that the UK have strict recording and monitoring regulations in place to give us accurate historical data. These models allow us and the Scottish Government to plan for different scenarios, and to assess the risks of disease thoroughly.

A number of the team are working on building models to analyse data where specific pieces of information are unknown, for example, BioSS are looking at mapping disease spread when the original host is unknown. By using the data and the knowledge we have about diseases and how they spread, we are able to build accurate mapping tools, even when bits of the jigsaw are missing.

We look very closely at animal networks using the movement data we have. By understanding the networks in Scotland and further afield, we can build an accurate picture of our livestock from birth / arrival right through to death / slaughter. This information is not only useful for understanding the potential spread of disease, but helps us to advise the Scottish Government on how risk can be reduced through movement restrictions. Applying movement restrictions is a very complex matter, and a great number of factors must be considered including potential environmental and economic impacts along with health considerations. Being able to understand how networks and animal movements impact on disease spread and control, allows us to give accurate advice on any required restrictions, their extent and length of time. The University of Glasgow are currently looking at animal standstills and the potential impact on the risk of Foot & Mouth Disease as an example.

The EPIC team also use data to develop machine learning techniques. Currently we are undertaking research looking at whether movement data can be used in machine learning to help predict disease breakdowns, led by the Roslin Institute.

Data comes in many forms and from many sources, but part of the remit of EPIC is to ensure that we maximise all the valuable data that we have access to, developing new techniques to analyse it in order to reduce disease risks for Scotland’s livestock population.

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