Sensitivity Analysis to Evaluate the Impact of Uncertain Factors in a Scenario Tree Model for Classical Swine Fever Introduction

• Published in: Risk analysis 2006-10, Vol.26 (5), p.1311-1322
• Main Authors: De Vos, Clazien J., Saatkamp, Helmut W., Nielen, Mirjam, Huirne, Ruud B. M.
• Format: Article
• Language: English
• Subjects: Animals; Belgium; Classical swine fever; Classical Swine Fever - epidemiology; Classical Swine Fever - transmission; Classical Swine Fever Virus; Classical swine fever virus - metabolism; design; Design of experiments; disease; Disease Outbreaks; Economics; epidemic; Epidemics; Germany; Illness; Infectious diseases; Livestock; Livestock industry; Models, Biological; Models, Theoretical; netherlands; Pig breeding; Probability; probability analysis; Public Health; Reproducibility of Results; Risk; Risk assessment; Risk Factors; Risk management; screening; Sensitivity analysis; Sensitivity and Specificity; Statistical methods; Stochastic Processes; stochastic simulation; strategies; Studies; Swine; Transportation; uncertainty; United Kingdom; virus
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/j.1539-6924.2006.00816.x

Introduction of classical swine fever virus (CSFV) is a continuing threat to the pig production sector in the European Union. A scenario tree model was developed to obtain more insight into the main risk factors determining the probability of CSFV introduction (PCSFV). As this model contains many uncertain input parameters, sensitivity analysis was used to indicate which of these parameters influence model results most. Group screening combined with the statistical techniques of design of experiments and meta‐modeling was applied to detect the most important uncertain input parameters among a total of 257 parameters. The response variable chosen was the annual PCSFV into the Netherlands. Only 128 scenario calculations were needed to specify the final meta‐model. A consecutive one‐at‐a‐time sensitivity analysis was performed with the main effects of this meta‐model to explore their impact on the ranking of risk factors contributing most to the annual PCSFV. The results indicated that model outcome is most sensitive to the uncertain input parameters concerning the expected number of classical swine fever epidemics in Germany, Belgium, and the United Kingdom and the probability that CSFV survives in an empty livestock truck traveling over a distance of 0–900 km.