Decisions regarding the implementation of prevention and intervention measures rely on the assessment through the analysis and interpretation of surveillance data of the epidemiological status of target populations or of focal units in target populations. Because they are generated through imperfect reporting, diagnosing, sampling and testing processes, surveillance data are most of the time non-exhaustive, partially distorted and, sometimes, non-representative. Therefore, even when perfectly tailored response mechanisms are planned, ineffective surveillance can result in misjudging an epidemiological situation and adopting inappropriate intervention measures. There is thus a widely recognized need for effective animal health surveillance. In this chapter, we focus on the quantitative methods to evaluate the effectiveness of actual or potential surveillance programs, and their applications. We first introduce the different attributes that reflect surveillance effectiveness. We then briefly describe the methods available to assess these attributes by covering methods that rely on the statistical modelling of the data generated by surveillance programmes (data-based methods) as well as on methods that imply the formalization of the surveillance process through probabilistic, mathematical or simulation models (process-based methods). We finally discuss the relevance of these methods with regard to the type of evaluation (ex-ante, in-itinere, ex-post), the evaluation question (effectiveness evaluation, effectiveness optimization) and the objective of the surveillance programme (early detection, case detection, freedom from disease, prevalence monitoring).