Ruth Cox

Multi-Criteria Decision Analysis Tools for Prioritising Emerging or Re-Emerging Infectious Diseases Associated with Climate Change in Canada

Global climate change is known to result in the emergence or re-emergence of some infectious diseases. Reliable methods to identify the infectious diseases of humans and animals and that are most likely to be influenced by climate are therefore required. Since different priorities will affect the decision to address a particular pathogen threat, decision makers need a standardised method of prioritisation. Ranking methods and Multi-Criteria Decision approaches provide such a standardised method and were employed here to design two different pathogen prioritisation tools. The opinion of 64 experts was elicited to assess the importance of 40 criteria that could be used to prioritise emerging infectious diseases of humans and animals in Canada. A weight was calculated for each criterion according to the expert opinion. Attributes were defined for each criterion as a transparent and repeatable method of measurement. Two different Multi-Criteria Decision Analysis tools were tested, both of which used an additive aggregation approach. These were an Excel spreadsheet tool and a tool developed in software ‘M-MACBETH’. The tools were trialed on nine ‘test’ pathogens. Two different methods of criteria weighting were compared, one using fixed weighting values, the other using probability distributions to account for uncertainty and variation in expert opinion. The ranking of the nine pathogens varied according to the weighting method that was used. In both tools, using both weighting methods, the diseases that tended to rank the highest were West Nile virus, Giardiasis and Chagas, while Coccidioidomycosis tended to rank the lowest. Both tools are a simple and user friendly approach to prioritising pathogens according to climate change by including explicit scoring of 40 criteria and incorporating weighting methods based on expert opinion. They provide a dynamic interactive method that can help to identify pathogens for which a full risk assessment should be pursued.

Modelling the impact of temperature-induced life history plasticity and mate limitation on the epidemic potential of a marine ectoparasite

Temperature is hypothesized to contribute to increased pathogenicity and virulence of many marine diseases. The sea louse (Lepeophtheirus salmonis) is an ectoparasite of salmonids that exhibits strong life-history plasticity in response to temperature; however, the effect of temperature on the epidemiology of this parasite has not been rigorously examined. We used matrix population modelling to examine the influence of temperature on demographic parameters of sea lice parasitizing farmed salmon. Demographically-stochastic population projection matrices were created using parameters from the existing literature on vital rates of sea lice at different fixed temperatures and yearly temperature profiles. In addition, we quantified the effectiveness of a single stage-specific control applied at different times during a year with seasonal temperature changes. We found that the epidemic potential of sea lice increased with temperature due to a decrease in generation time and an increase in the net reproductive rate. In addition, mate limitation constrained population growth more at low temperatures than at high temperatures. Our model predicts that control measures targeting preadults and chalimus are most effective regardless of the temperature. The predictions from this model suggest that temperature can dramatically change vital rates of sea lice and can increase population growth. The results of this study suggest that sea surface temperatures should be considered when choosing salmon farm sites and designing management plans to control sea louse infestations. More broadly, this study demonstrates the utility of matrix population modelling for epidemiological studies.

The Use of Expert Opinion to Assess the Risk of Emergence or Re-Emergence of Infectious Diseases in Canada Associated with Climate Change

Global climate change is predicted to lead to an increase in infectious disease outbreaks. Reliable surveillance for diseases that are most likely to emerge is required, and given limited resources, policy decision makers need rational methods with which to prioritise pathogen threats. Here expert opinion was collected to determine what criteria could be used to prioritise diseases according to the likelihood of emergence in response to climate change and according to their impact. We identified a total of 40 criteria that might be used for this purpose in the Canadian context. The opinion of 64 experts from academic, government and independent backgrounds was collected to determine the importance of the criteria. A weight was calculated for each criterion based on the expert opinion. The five that were considered most influential on disease emergence or impact were: potential economic impact, severity of disease in the general human population, human case fatality rate, the type of climate that the pathogen can tolerate and the current climatic conditions in Canada. There was effective consensus about the influence of some criteria among participants, while for others there was considerable variation. The specific climate criteria that were most likely to influence disease emergence were: an annual increase in temperature, an increase in summer temperature, an increase in summer precipitation and to a lesser extent an increase in winter temperature. These climate variables were considered to be most influential on vector-borne diseases and on food and water-borne diseases. Opinion about the influence of climate on air-borne diseases and diseases spread by direct/indirect contact were more variable. The impact of emerging diseases on the human population was deemed more important than the impact on animal populations.

Use of Bayesian Belief Network techniques to explore the interaction of biosecurity practices on the probability of porcine disease occurrence in Canada

Abstract Identification and quantification of pathogen threats need to be a priority for the Canadian swine industry so that resources can be focused where they will be most effective. Here we create a tool based on a Bayesian Belief Network (BBN) to model the interaction between biosecurity practices and the probability of occurrence of four different diseases on Canadian swine farms. The benefits of using this novel approach, in comparison to other methods, is that it enables us to explore both the complex interaction and the relative importance of biosecurity practices on the probability of disease occurrence. In order to build the BBN we used two datasets. The first dataset detailed biosecurity practices employed on 218 commercial swine farms across Canada in 2010. The second dataset detailed animal health status and disease occurrence on 90 of those farms between 2010 and 2012. We used expert judgement to identify 15 biosecurity practices that were considered the most important in mitigating disease occurrence on farms. These included: proximity to other livestock holdings, the health status of purchased stock, manure disposal methods, as well as the procedures for admitting vehicles and staff. Four diseases were included in the BBN: Porcine reproductive and respiratory syndrome (PRRS), (a prevalent endemic aerosol pathogen), Swine influenza (SI) (a viral respiratory aerosol pathogen), Mycoplasma pneumonia (MP) (an endemic respiratory disease spread by close contact and aerosol) and Swine dysentery (SD) (an enteric disease which is re-emerging in North America). This model indicated that the probability of disease occurrence was influenced by a number of manageable biosecurity practices. Increased probability of PRRS and of MP were associated with spilt feed (feed that did not fall directly in a feeding trough), not being disposed of immediately and with manure being brought onto the farm premises and spread on land adjacent to the pigs. Increased probabilities of SI and SD were associated with the farm allowing access to visiting vehicles without cleaning or disinfection. SD was also more likely to occur when the health status of purchased stock was not known. Finally, we discuss how such a model can be used by the Canadian swine industry to quantify disease risks and to determine practices that may reduce the probability of disease occurrence.