Pairwise summation as a method for the additive combination of probabilities in qualitative risk assessments.

Qualitative 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.

Knowledge gaps in invasive species infections: Alien mammals of European Union concern as a case study.

Invasive Alien Species (IAS), i.e. species introduced by humans outside their natural geographic range, may act as host or vectors of pathogens of both human and animal health relevance. Although it has been recognized that IAS should deserve more attention from a public and animal health perspective, data on the pathogens hosted by these species are not systematically collected and this prevents accurate assessments of IAS-specific risks of disease transmission. To support the future development of disease risk assessments, we systematically reviewed the scientific literature related to the pathogens of the eleven mammal species included in the European list of IAS of concern to gain insight in the amount and quality of data available. Data were analyzed to assess the current knowledge on the pathogens harbored by mammal IAS in natural conditions, through the identification of the main factors associated with research intensity on IAS pathogens and with the IAS observed pathogen species richness, the estimation of the true pathogen species richness for each IAS, and a meta-analysis of prevalence for the pathogens of health relevance. While the review confirmed that mammal IAS harbor pathogens of human and animal health relevance such as rabies virus, West Nile Virus, Borrelia burgdorferi and Mycobacterium bovis, results also highlighted strong information gaps and biases in research on IAS pathogens. In addition, the analyses showed an underestimation of the number of pathogens harbored by these species and the existence of high levels of uncertainty in the prevalence of the pathogens of health significance identified. These results highlight the need towards more efforts in making the available information on IAS pathogens accessible and systematically collected in order to provide data for future investigations and risk assessments, as well as the need of relying on alternative sources of information to assess IAS disease risk, like expert opinions.

How to choose the best control strategy? Mathematical models as a tool for pre-intervention evaluation on a macroparasitic disease.

During the last century, emerging diseases have increased in number, posing a severe threat for human health. Zoonoses, in particular, represent the 60% of emerging diseases, and are a big challenge for public health due to the complexity of their dynamics. Mathematical models, by allowing an a priori analysis of dynamic systems and the simulation of different scenarios at once, may represent an efficient tool for the determination of factors and phenomena involved in zoonotic infection cycles, but are often underexploited in public health. In this context, we developed a deterministic mathematical model to compare the efficacy of different intervention strategies aimed at reducing environmental contamination by macroparasites, using raccoons (Procyon lotor) and their zoonotic parasite Bayilsascaris procyonis as a model system. The three intervention strategies simulated are raccoon depopulation, anthelmintic treatment of raccoons and faeces removal. Our results show that all these strategies are able to eliminate the parasite egg population from the environment, but they are effective only above specific threshold coverages. Host removal and anthelmintic treatment showed the fastest results in eliminating the egg population, but anthelmintic treatment requires a higher effort to reach an effective result compared to host removal. Our simulations show that mathematical models can help to shed light on the dynamics of communicable infectious diseases, and give specific guidelines to contain B. procyonis environmental contamination in native, as well as in new, areas of parasite emergence. In particular, the present study highlights that identifying in advance the appropriate treatment coverage is fundamental to achieve the desired results, allowing for the implementation of cost- and time-effective intervention strategies.