Risks can be reduced through targeted interventions, e.g. communication campaigns, so that passengers understand the risks of their actions and the penalties if they get caught, and surveillance at ports of entry (detection dogs, luggage inspections, self-declarations, interviews, etc.). These interventions are most effective when targeted towards high-risk passengers during high-risk periods and at high-risk ports of entry. Therefore, risk assessment can help to target communication campaigns and surveillance activities to make them more effective. A targeted approach supports greater efficiency, making it particularly applicable and useful in low-resource settings.

Existing data sources and new methodologies (for example machine learning methods and interconnection of databases) allow faster and better-quality assessments. A quantitative stochastic risk assessment model has been developed to estimate the risk of ASFv introduction into the United States of America by illegal swine products carried by air passengers [1]. The results showed that it was possible to identify the countries of origin, airports and months that presented a higher risk for the introduction of infected products. The model can be adapted to other settings and easily updated with new data (as demonstrated by the authors after the introduction of ASF into the People’s Republic of China [2]) to implement and feed real-time surveillance systems. This could potentially help customs to increase the detection rate of smuggled products, by indicating when and where to look for them.

Without targeting of activities and resources, controlling this route can be very difficult because of the numerous limitations (personnel, resources, low sensitivity of detection, time for controls, etc.).
 

http://dx.doi.org/10.20506/bull.2020.2.3153