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AbstractQualitative frameworks are widely employed to tackle urgent animal or public health issues when data are scarce and/or urgent decisions need to be made. In qualitative models, the degree of belief regarding the probabilities of the events occurring along the risk pathway(s) and the outcomes is described in nonnumerical terms, typically using words such as Low, Medium, or High. The main methodological challenge, intrinsic in qualitative models, relates to performing mathematical operations and adherence to the rule of probabilities when probabilities are nonnumerical. Although methods to obtain the qualitative probability from the conditional realization of n events are well‐established and consistent with the multiplication rule of probabilities, there is a lack of accepted methods for addressing situations where the probability of an event occurring can increase, and the rule of probability P(AUB) = P(A) + P(B) − P(A∩B) should apply. In this work, we propose a method based on the pairwise summation to fill this methodological gap. Our method was tested on two qualitative models and compared by means of scenario analysis to other approaches found in literature. The qualitative nature of the models prevented formal validation; however, when using the pairwise summation, results consistently appeared more coherent with probability rules. Even if the final qualitative estimate can only represent an approximation of the actual probability of the event occurring, qualitative models have proven to be effective in providing scientific‐based evidence to support decision‐making. The method proposed in this study contributes to reducing the subjectivity that characterizes qualitative models, improving transparency and reproducibility.

Transmissible spongiform encephalopathies (TSEs) are an important public health concern. Since the emergence of bovine spongiform encephalopathy (BSE) during the 1980s and its link with human Creutzfeldt–Jakob disease, active surveillance has been a key element of the European Union's TSE control strategy. Success of this strategy means that now, very few cases are detected compared with the number of animals tested. Refining surveillance strategies would enable resources to be redirected towards other public health priorities. Cost-effectiveness analysis was performed on several alternative strategies involving reducing the number of animals tested for BSE and scrapie in Great Britain and, for scrapie, varying the ratio of sheep sampled in the abattoir to fallen stock (which died on the farm). The most cost-effective strategy modelled for BSE involved reducing the proportion of fallen stock tested from 100% to 75%, producing a cost saving of ca GBP 700,000 per annum. If 50% of fallen stock were tested, a saving of ca GBP 1.4 million per annum could be achieved. However, these reductions are predicted to increase the period before surveillance can detect an outbreak. For scrapie, reducing the proportion of abattoir samples was the most cost-effective strategy modelled, with limited impact on surveillance effectiveness.

International organizations and national governments in resource-scarce settings regularly support programs for the control of animal diseases with the aim of improving smallholder food security. However, the impact of such disease control programs on smallholder food security remains unclear. Mixed methods designs that integrate the collection, analysis and interpretation of qualitative and quantitative data in a single study, are increasingly being used to achieve deeper explorations of complex topics. We propose a mixed methods design to assess the four pillars of food security and coping strategies among smallholders. The methodology is illustrated with a case study in the context of a transnational program for the control of Foot and Mouth Disease (FMD) in the Andean region, involving interviews with 632 smallholders in three countries. Quantitative data were analysed using multivariate analysis to describe smallholders' profiles. Food Consumption Score (FCS) was calculated for each household. The qualitative phase involved developing themes to characterise these smallholders' experiences using Thematic Analysis. Food acquisition capacity and coping strategies varied greatly across smallholders. Only nine (1.4%) of households had a FCS below the acceptable threshold, however, food stability was compromised across study areas. Household production, financial capacity, household demographics and food prices were the main factors influencing variation in food consumption. The case study presented here illustrates the use of a mixed methods approach to assess the four dimensions of food security and categorise key differences across smallholders during a single visit.

International organizations and national governments in resource-scarce settings regularly support programs for the control of animal diseases with the aim of improving smallholder food security. However, the impact of such disease control programs on smallholder food security remains unclear. Mixed methods designs that integrate the collection, analysis and interpretation of qualitative and quantitative data in a single study, are increasingly being used to achieve deeper explorations of complex topics. We propose a mixed methods design to assess the four pillars of food security and coping strategies among smallholders. The methodology is illustrated with a case study in the context of a transnational program for the control of Foot and Mouth Disease (FMD) in the Andean region, involving interviews with 632 smallholders in three countries. Quantitative data were analysed using multivariate analysis to describe smallholders' profiles. Food Consumption Score (FCS) was calculated for each household. The qualitative phase involved developing themes to characterise these smallholders' experiences using Thematic Analysis. Food acquisition capacity and coping strategies varied greatly across smallholders. Only nine (1.4%) of households had a FCS below the acceptable threshold, however, food stability was compromised across study areas. Household production, financial capacity, household demographics and food prices were the main factors influencing variation in food consumption. The case study presented here illustrates the use of a mixed methods approach to assess the four dimensions of food security and categorise key differences across smallholders during a single visit.