US-UK Collab Linking models and policy: Using active adaptive management for optimal control of disease outbreaks.

In the event of an outbreak of an infectious disease, management strategies to control further spread of infection are generally implemented based either upon strategies employed during previous epidemics or a pre-conceived expectation of the likelihood of success. However, at the onset of an outbreak, there is a great amount of uncertainty regarding the epidemiological properties of the disease and this may have a significant influence upon the ability of the chosen management strategy to contain or control the epidemic. Mathematical models can be developed to simulate spread of disease and evaluate the effectiveness of potential control strategies. However, the effectiveness of these models may be restricted by our limited knowledge of the epidemic as it unfolds.
Extensive analyses of the 2001 Foot and Mouth (FMD) outbreak in the UK have provided valuable information about both the dynamics of disease spread and the implementation of management actions. However, those observations are specific both to the UK setting and to the strain of. A future outbreak in the UK or an outbreak in another country such as the US will not necessarily follow the same pattern. Thus, key aspects of disease spread, and the optimal response, cannot be resolved until an outbreak occurs. Adaptive management (AM) seeks to address this limitation by incorporating monitoring, evaluation, and response into management actions such that management strategies can be modified and updated in response to improved understanding of the outbreak dynamics. The AM framework has previously been applied in conservation management but is yet to be applied to the management of infectious diseases.
AM provides a framework for switching from the early strategy that optimises the average outcome (when uncertainties are yet to be resolved), to the one that optimises the outcome for the specific model (or models) that best matches by the outbreak at hand. Additionally, active adaptive management seeks to make this switch as soon as possible, by initially using sub-optimal controls that allow the specific model to be identified as soon as possible. Thus, early management actions can be used to improve knowledge of the dynamics and more rapidly transition to the strategy that maximizes the global objective.
Although we are interested in the general application of AM to a range of outbreak scenarios; in this project we will use the 2001 FMD epidemic as a detailed, well-defined example. Despite a decade of modelling efforts, key uncertainties concerning optimal control remain, AM will allow us to address these issues. In particular we propose to:
1. Use the observed surveillance from the 2001 outbreak to identify the optimal adaptive strategy and the economic benefit of that strategy relative to a static (fixed) strategy.
2. Simulate the use of active AM to discriminate amongst competing models and selection of the optimal strategy. To that end we will consider the application of management strategies to facilitate learning and rapid updating of control policies.
3. Use AM to determine optimal management strategies for other disease scenarios, helping to generate a more generic understanding.
4. Using the FMD case-study developed in 1 and 2, we will support workshops that engage members of the US and UK policy community in the use of adaptive management for an outbreak.
5. Based on the understanding gained in the workshops, we will develop a US-based outbreak case-study that will be used as the subject of training workshops in the second half of the grant period. This case study would demonstrate the utility of AM in a scenario of extreme uncertainty.
The outputs of this project would elucidate the ability of AM to provide efficient policy advice in the event of future unknown outbreaks of infectious disease. A single, flexible policy that is able to adapt to the observed outbreak would have massive implications in reducing the impact of future outbreaks.

In the event of an outbreak of infectious disease, it is necessary to make timely, and potentially critical decisions in the face of uncertainty. Detailed models of epidemic dynamics can be valuable in exploring outbreak scenarios and evaluating candidate strategies but are fundamentally limited by our lack of knowledge of the epidemic system before an outbreak has occured. Monitoring the response of epidemic dynamics to management interventions can often reduce the uncertainty encapsulated in competing models. In practice, however, such evaluation and assessment of competing models is often done in retrospect, and is thus of little use to the application of management in real-time. Large scale spatial epidemics require immediate management action. However, careful monitoring of the outbreak response to management allows for the opportunity to learn about the epidemiology of the epidemic and to modify management actions accordingly.
In this project we propose a structured decision-making framework for evaluating and updating management policies in the light of competing, dynamic epidemic models. The key to this adaptive management approach is linking predictive quantitative modelling with sequential evaluation of management objectives. We will develop this framework in the context of a very specific agricultural health setting, Foot and Mouth Disease (FMD). The 2001 UK FMD epidemic provides a comprehensive data set that will allow us to implement detailed analyses of optimal control in the presence of uncertainty. However, we will also use generic, theoretical models of spatial epidemic dynamics to study more generally the impact of uncertainty on the outbreak response and the ability to develop responsive control strategies. In doing so, we will develop algorithms and software to study generic questions about optimal response to spatial epidemics that can be applied to agricultural, veterinary-health (and human health) management in a variety of settings.

In early February 2001, Foot-and-Mouth Disease (FMD) entered the United Kingdom. The subsequent epidemic lasted until the beginning of October and caused huge devastation, not only to the UK livestock industry but for the export market, the tourist trade and the economy in general. When the first case was reported, movements of all animals between farms ceased, whilst all livestock exports where halted. This resulted in huge economic losses for both farmers and the export market. Similarly, access to the countryside was severely restricted, which limited the tourist trade. Many businesses which relied heavily on tourism struggled to survive and the UK government found itself facing a potentially huge economic crisis.

In the early stages of the epidemic, the UK government consulted members of the mathematical modelling community for advice regarding the optimal strategy to control the epidemic. Since 2001, policy makers from around the world have developed their own contingency plans for control of FMD, incorporating control policies developed with the assistance of mathematical modellers with experience from the 2001 epidemic. Whilst culling strategies were modified in 2001 in response to the perceived failure of strategies introduced at the onset of the epidemic, there was little attempt to learn from observations of early epidemic behaviour and modify management strategies in a rigorous way.

This project has huge benefit to a wide range of groups. Mathematical modelling of infectious diseases is a rapidly growing field, but to date there has been little work done on rigorous optimisation of control policies in the event of epidemiological and demographic uncertainties. As such, this work will provide invaluable insights to the epidemiological and wider scientific community into the successful development of models for use with a wide range of diseases and and for a range of farm demographies.

In the event of any future outbreak of FMD in the US or UK livestock industry, policy makers would again seek advice from mathematical modellers regarding optimal control of disease. Prompt, efficient control can have a huge effect on reducing the overall loss of livestock and the economic cost of any epidemic and detailed numerical models are relied upon (in both the UK and elsewhere) to provide informed advice. This project therefore has the potential to provide a huge benefit to the US and UK governments and policy makers around the world.

As a consequence of the implementation of efficient strategies to control disease, there will be a subsequent reduction in the total number of farms affected, the duration of any epidemic and the spatial spread of disease. This research would therefore provide a huge benefit to the farming community, the tourist industry and the export market.