Economics of reducing response time to foreign-animal disease in the United States with point-of-care diagnostic tests

As low probability events, United States producers, value chain actors, and veterinary services (VS) have limited experience with identifying foreign animal disease (FAD), which can allow FADs to spread undetected. Point-of-care (POC) diagnostic testing may help reduce the time from detecting an initial suspect case to implementing actionable interventions compared to the current approach of only using laboratory diagnostic testing for disease diagnosis and confirmation. To evaluate the value of the reduced response time, we compare the associated costs between the two diagnostic approaches while accounting for the uncertainty surrounding the size of a FAD event. We apply a state-contingent approach (SCA) to model the uncertainty surrounding a FAD through alternative events, where the event defines the scale of outbreak size and its duration. We apply this approach within a cost-benefit framework (CBA) to determine the economic value from the two testing investment strategies to help explain the policymaker’s response (and costs) to alternative FAD events while also considering the cost impacts on the producers from each event. Compared to the current laboratory strategy, a POC strategy that reduces response time by 0.5-days (swine, cattle scenarios) and 1.5-days (poultry scenario) may provide cost-saving to both producers and public response efforts. The benefit-cost analysis further suggests that despite the higher fixed costs to adopt the POC strategy, the swine and cattle sectors may benefit while the benefits may not be as pronounced in the poultry sector. POC testing that can reduce the time between detection and response during a FAD event may be a sound strategy for public expenditure and provide cost-savings for producers, especially when minimal fixed costs are incurred. However, to fully determine the value of POC testing, the consequences (costs) associated with potential actions if something goes wrong, (e.g. false positive results), should be considered in future studies.