Uppsala Health Summit

Mathematics key in verifying contingency plans for Foot-and-Mouth disease

2017-05-05

Foot-and-Mouth (FMD) disease is the most contagious disease of mammals. The disastrous consequences of the outbreak in the UK in 2001 remind us that we must be prepared, at all times, to detect and respond to a possible introduction of the virus.

Dr. Fernanda Dórea, National
Veterinary Institute

Contingency plans are a central tool in a One Health toolbox. But what measures should be included in an optimal plan? To answer this question, Dr. Fernanda Dórea and her team at at the National Veterinary Institute, SVA, and the Swedish Board of Agriculture, have used mathematical models. These models make use of information  regarding the movement of animals and people between farms in Sweden. Their conclusion was that an outbreak of FMD can be controlled within three weeks following detection. The control measures would include restriction of movement for susceptible animals and tracking of contacts, but additional measures, such as emergency vaccination or pre-emptive depopulation, are not likely to be needed. Mathematical models will become increasingly important in disease control planning, especially if we also can identify how to use data that is already available from various digital sources.

- We need to get policy makers globally on board to understand the opportunities, and start discussing the necessary policy frameworks for adoption of data-driven decision, says Dr. Fernanda Dórea.

At Uppsala Health Summit 10 - 11 October, Dr. Dórea will coordinate a workshop on how we can improve decision-making in surveillance using innovations in big data, building a framework for data-driven surveillance.

Considering the devastating effects an outbreak of Foot-and-Mouth disease (FMD) may have, all EU member states are required to have a contingency plan against FMD. Dr Dórea and her research team wanted to develop a balanced contingency plan, a plan that would limit the spread of the disease in the event of an outbreak, and where costs (financial and in animal welfare) wouldn’t outweigh the benefits. The researchers set up two questions:
1) If the FMD virus were introduced in Sweden, how would the epidemic develop – how fast and how wide would it spread?

2) What would be the most cost-effective strategy to eradicate the virus, in particular, should Sweden prepare for vaccination or even euthanasia and destruction of all susceptible animals around infected farms?

According to current regulations,  all movement of susceptible animals in the country must be stopped for three days directly following detection of the first farm with FMD infected animals. In addition, all animals at the infected farm must be humanely destroyed, and all contacts with the infected farm investigated (movement of animals, people, and service vehicles). All farms within a radius of 10km from the infected farm would be visited by a veterinarian team twice in an interval of 2-4 weeks. Any further infected farms during this investigation process would be subjected to the same measures.

The researchers spent one year collecting data about the susceptible animal population in Sweden (cattle, pigs and small ruminants) and the patterns of movements of animals and people between farms. To analayze the data collected, the epidemiologists at the National Veterinary Institute used a mathematical model capable of reproducing the disease spread conditions. The model was originally developed by researchers in the USA, and subsequently adapted by researchers in Denmark.

The conclusion was that an FMD outbreak in Sweden can be controlled within the three weeks following the detection if the described measures are implemented. Additional measures, such as pre-emptive depopulation (destroying all susceptible animals in a given radius from infected farms, regardless of whether they are suspected to be infected or not) or emergency vaccination would not increase the efficiency.

-  Applying mathematical models to data gathered is of immense value to animal and public health. Therefore we want to take this a step further and discuss how we can gather and analyse large volumes of data – big data – efficiently and timely to improve systems for surveillance.

Efficient use of data for One Health decision-making is the topic of the workshop at Uppsala Health Summit, Innovation and Big Data in Health Surveillance organized by Dr. Fernanda Dórea, together with Associate professor John Östh, researcher at Uppsala University’s department for Economic geography.

- We aim for a dialogue on the challenges for implementing decision-making based on different sources of big data, and strive to prioritize which challenges to start working with, says Dr Dórea. Data, such as mobile and animal movement data, can help us prevent, detect and control infectious diseases spread.

- This is, she continues, a typical example of how multi-disciplinary research has lead to innovative thinking on surveillance.

Such models can prove to be extremely useful for both high- and low income settings, but it is critical to discuss their use in a global policy context.