Decision-making for foot-and-mouth disease control: Objectives matter

Abstract

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Keywords: Decision making; Epidemiology; Foot-and-mouth disease; Management; Objectives; Optimisation.

Figure 1

Cost of both compensation for culled livestock and vaccine doses administered (£ million), for a range of control actions (grouped rows) evaluated against three objectives (columns) under five models (A–E, rows). Column titles represent the statistic used for ranking the control actions. Cells are rounded to integer values, and are coloured according to rank (within both a model and objective) with red representing worst performing control actions and blue representing best performing control action. The final column is the empirical probability of the combined cost of compensation of culled livestock and vaccine doses administered being greater than £20 million.

Figure 2

Outbreak duration (days) of control actions (grouped rows) evaluated against several objectives (columns) under five models (rows). Column titles represent the statistic used for ranking the control actions. Cells are rounded to integer values, and are coloured according to rank with red representing worst performing control actions and blue representing best performing control action (within both a model and objective). The final column is the empirical probability of the outbreak duration being longer than 50 days.

Figure 3

Median performance of control actions (grouped rows) evaluated against several objectives under five models (rows). The median is used as the statistic to form a ranking of control actions under each metric (columns). Column titles represent the metric use for the ranking, and units for each column are respectively: £ million, days, and thousand head of livestock culled. Cells are rounded to integer values and are coloured according to rank within each model and objective (red signifies worst performing action, blue signifies best performing action).

Figure 4

Performance of control strategies under both mean outbreak duration and mean number of livestock culled across five simulation models of FMD spread. Mean values from simulation output under each model and under each measure of success (outbreak duration and livestock culled) have been scaled so that the worst control strategy has a score of 1 and the best a score of 0 within each model.