Application of portfolio theory to risk-based allocation of surveillance resources in animal populations

Distribution of finite levels of resources between multiple competing tasks can be a challenging problem. Resources need to be distributed across time periods and geographic locations to increase the probability of detection of a disease incursion or significant change in disease pattern. Efforts should focus primarily on areas and populations where risk factors for a given disease reach relatively high levels. In order to target resources into these areas, the overall risk level can be evaluated periodically across locations to create a dynamic national risk landscape. Methods are described to integrate the levels of various risk factors into an overall risk score for each area, to account for the certainty or variability around those measures and then to allocate surveillance resources across this risk landscape. In addition to targeting resources into high risk areas, surveillance continues in lower risk areas where there is a small yet positive chance of disease occurrence. In this paper we describe the application of portfolio theory concepts, routinely used in finance, to design surveillance portfolios for a series of examples. The appropriate level of resource investment is chosen for each disease or geographical area and time period given the degree of disease risk and uncertainty present.