Roadmap for the research to underpin the development of control strategies for FMD

What are we trying to achieve and why? What is the problem we are trying to solve?

To develop models and analytical tools to identify the most appropriate, cost-effective surveillance and control strategies, supporting the decision-making process and optimising the use of resources and efforts.

What are the scientific and technological challenges (knowledge gaps needing to be addressed)?

• Develop analytical tools to support the decision-making process, including, a) anomaly detection methods to identify outlier events; b) prediction models for identification of genetic variants of viruses, to predict severity, duration, and likelihood of transmission of disease, and to evaluate the degree of success of control and prevention interventions; c) epidemiological models that project spread of disease in a defined region under various control strategies and that can be used in developing disease control programmes and for active surveillance sampling.
• Apply epidemiological models to identify key areas of the world to be targeted for active collection of samples and information, and for monitoring the evolution of the disease as part of the global FMD surveillance system in critical regions of the world.
• Develop models to predict the spread of FMDV lineages and highlighting risk.
• Develop an epidemiological model that can reduce the sampling to determine the sanitary status.
• Develop intervention models to improve understanding of endemic circulation of FMDV, to estimate critical community sizes and identify appropriate control points.
• Improve interpretation of trees, detect rapid evolutionary changes, and understand the effects of under-reporting cases which help to develop predictive models in case of intervention policies (vaccinate to live or sanitary killing, for instance).

What approaches could/should be taken to address the research question?

Use of new generation of simulation (‘intelligent’) models, having the ability to capture information emerging from the field in the face of an epidemic, to use that information to adapt the model parameters (‘learning’), to modify model assumptions, including those related with the characteristic of the strain causing the outbreak, and to produce updates in near-real time that correct previous estimates of the expected evolution of the epidemic

What else needs to be done before we can solve this need?

• Prevalence of FMD.
• Improved knowledge on FMD epidemiology

Existing knowledge including successes and failures

Over the past years, there has been significant research in mathematical modelling for FMD, mostly on transmission
models to predict the consequences of outbreaks in FMD-free countries, estimate resource requirements and compare
different control options, particularly the impact of vaccination.