El Allaki Farouk, Christensen Jette, Vallières André
Terrestrial Animal Health Epidemiology and Surveillance Section, Canadian Food Inspection Agency, 3200 Sicotte St., P.O. Box 5000, St-Hyacinthe, QC, J2S 7C6, Canada.
Terrestrial Animal Health Epidemiology and Surveillance Section, Canadian Food Inspection Agency, Department of Health Management, Atlantic Veterinary College, 550 University Ave., Charlottetown, PEI, C1A 4P3, Canada.
Prev Vet Med. 2019 Apr 1;165:36-43. doi: 10.1016/j.prevetmed.2019.02.006. Epub 2019 Feb 10.
To achieve an appropriate and efficient sample in a surveillance program, the goals of the program should drive a careful consideration of the selection method or combination of selection methods to be applied. Therefore, the ongoing analysis and assessment of a surveillance system may include an assessment of the ability of the applied selection methods to generate an appropriate sample. There may be opinions from many technical experts (TEs) and many criteria to consider in a surveillance system so there is a need for methods to combine knowledge, priorities and preferences from a group of TEs. This paper proposes a modified weighted and unweighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis to choose selection methods in surveillance. An example from the Canadian Notifiable Avian Influenza surveillance (CanNAISS) is used to illustrate the method as this surveillance offers unique data with multiple selection methods and subpopulations. The primary objective was to assess the performance of the different selection methods applied in CanNAISS, from 2008 to 2013, in three subpopulations (A-C). A modified TOPSIS (weighted and unweighted) analyses is proposed to aggregate preferences from three TEs and to identify the selection method that was closest to the ideal solution agreed upon by the TEs. Criteria weights were provided individually by three TEs. For the group decision making, internal and external aggregation approaches were used with arithmetic and geometric means. The results of the weighted modified TOPSIS analysis showed that the selection methods that used farm registries yielded high estimates of the relative closeness to ideal-solution. The ranking of selection methods based on the modified TOPSIS weighted analysis, conducted at the individual and group decision making levels were similar. Regardless of the aggregation approach used (internal or external) in group decision making, the use of the arithmetic and geometric means yielded similar estimates of relative closeness to ideal-solution. The unweighted modified TOPSIS analysis yielded similar estimates of the relative closeness to the ideal-solution and therefore making the interpretation of the results difficult. The weighted modified TOPSIS analysis contributed to an informed decision on the best selection method to apply in CanNAISS. The weighted modified TOPSIS analysis is a straightforward and suitable technique to address decision making problems where the profile of the ideal and non-ideal solutions is known a priori by the decision makers.
为在监测计划中获得合适且高效的样本，该计划的目标应促使对拟应用的选择方法或选择方法组合进行审慎考虑。因此，对监测系统的持续分析与评估可能包括对所应用选择方法生成合适样本能力的评估。在监测系统中可能存在众多技术专家（TE）的意见以及诸多需要考虑的标准，所以需要有方法来整合一组TE的知识、优先级和偏好。本文提出一种改进的加权和非加权TOPSIS（逼近理想解排序法）分析，用于在监测中选择选择方法。以加拿大法定禽流感监测（CanNAISS）为例来说明该方法，因为此监测提供了具有多种选择方法和亚群体的独特数据。主要目标是评估2008年至2013年在CanNAISS中应用于三个亚群体（A - C）的不同选择方法的性能。提出一种改进的TOPSIS（加权和非加权）分析，以汇总三位TE的偏好，并确定最接近TE们商定的理想解的选择方法。标准权重由三位TE分别提供。对于群体决策，使用算术平均值和几何平均值的内部和外部汇总方法。加权改进TOPSIS分析的结果表明，使用农场登记册的选择方法得出的相对接近理想解的估计值较高。基于改进TOPSIS加权分析在个体和群体决策层面进行的选择方法排名相似。在群体决策中，无论使用何种汇总方法（内部或外部），算术平均值和几何平均值的使用得出的相对接近理想解的估计值相似。非加权改进TOPSIS分析得出的相对接近理想解的估计值相似，因此难以对结果进行解释。加权改进TOPSIS分析有助于就CanNAISS中应用的最佳选择方法做出明智决策。加权改进TOPSIS分析是一种直接且合适的技术，可用于解决决策者事先已知理想解和非理想解概况的决策问题。
