Increasing demands for cost-effectiveness in surveillance for human health hazards can be met by introducmg risk-based principles. This implies targeting subpopulations w1th higher risk of infection compared to the whole population. We demonstrate how historical data from surveillance can be used to assess risk of infection. The model is called "Discounting historical evidence" and depends mainly on two variables: Annual risk of introduction Plntro and surveillance system sensitivity SSe (ability to detect infection if present). The model Implies simulations that reiterate for a number of years, and for each year the output is updated with the confidence on absence of infection. Trichinella spiralis infection in pigs is used as an example. In Denmark, pigs at slaughter are tested (currently 23 million per year), and despite of > 70 years of sampling no pigs have been found positive. Hence, we concluded that Plntro is low. SSe can be estimated from the maximum number of infected carcasses expected under the specified design prevalence, and the sensitivity of the test applied. According to the assessment, the prevalence of Trichmella in Danish pigs is negligible (<1 case/million). Based on this , a risk-based surveillance programme for Trichinella is designed that targets all out-door reared pigs as well as all sows and boars (currently 610,000 per year). Compared to confined pigs, outdoor-reared pigs have higher risk of getting Trichinella because of their exposure to wildlife, which might harbour Trichinella. Sows and boars are at increased risk, because they live longer than fimshers. Again, SSe and Plntro are estimated and the model is used to show how risk-based surveillance can be applied without jeopardizing human health. Finally, we incorporate wildlife surveys and test quality assurance in the programme. The model results are included in an application to the European Commission concern1ng Denmark's status as a region with negligible risk of Trichinella.