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. 2013 Aug 7;8(8):e68338.
doi: 10.1371/journal.pone.0068338. eCollection 2013.

Multi-criteria decision analysis tools for prioritising emerging or re-emerging infectious diseases associated with climate change in Canada

Ruth Cox  1 , Javier Sanchez, Crawford W Revie
Affiliations

Multi-criteria decision analysis tools for prioritising emerging or re-emerging infectious diseases associated with climate change in Canada

Ruth Cox et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Questionnaire designed to collect expert opinion about infectious disease characteristics for disease prioritisation in Canada.
Left hand column: criteria; right hand columns: criteria attributes. Numbers next to tick boxes indicate the value assigned to each attribute in the spreadsheet tool.
Figure 2
Figure 2. Spreadsheet tool to assess the risk of emergence or re-emergence of infectious diseases associated with climate change.
Figure 3
Figure 3. Decision tree structure (showing some of the criteria) developed in the software M-MACBETH.
Branches of the decision tree with a light blue branch are criteria, those with a yellow branch are attributes within one criterion.
Figure 4
Figure 4. Properties of criteria A4: “Current incidence of human disease in Canada”, showing the 6 different attributes.
Lower (blue) and upper (green) act as the scale's arbitrary values of 0 and 100 respectively.
Figure 5
Figure 5. Matrix of attributes for criterion A4 indicating the difference between each attribute.
The difference in value of two attributes is either ‘positive’ (i.e. one is greater than the other e.g. >20 is greater than 1–20) or where there is ‘no’ difference in value between two attributes. In this case there is no difference between answers of ‘0’, ‘not applicable’ or ‘unknown’.
Figure 6
Figure 6. M-MACBETH derived scores as they were allocated to criterion attributes in the matrix.
Figure 7
Figure 7. Disease ranking calculated in the spreadsheet tool for nine diseases.
A: Criteria were weighted using a fixed mean value based on expert opinion (weighting method 1). The maximum score possible for any disease was 23.7. B: Criteria were weighted using a probability distribution representing the range of expert opinion (weighting method 2). Cumulative probability distribution shows the total score over 10,000 iterations for each disease. The maximum score of a disease was a mean of 23.5 (standard deviation ±2.37, 95th percentile = 27.2 after 10,000 iterations).
Figure 8
Figure 8. Disease ranking by criteria grouping calculated in the spreadsheet tool for nine diseases.
Criteria were weighted using a probability distribution representative of expert opinion. Cumulative probability distribution shows the score for each disease during 10,000 iterations. Legends show pathogen ranking.
Figure 9
Figure 9. Total score compared to the ‘influence of climate’ score for each of nine diseases in the MACBETH tool.
West Nile virus was the highest ranking disease overall and the disease most likely to be influenced by climate.
Figure 10
Figure 10. Difference profile of Lyme disease compared to Chagas disease.
Bars indicate the difference in the score of two diseases for each criterion. A score of 0 (i.e. no bar) indicates that the two diseases scored the same. A green bar indicates that Lyme scored higher than Chagas, while orange bars indicate that Chagas scored higher than Lyme.
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Publication types

Grants and funding

Funding for this research was provided by the Public Health Agency of Canada (www.publichealth.gc.ca), grant number: 4500217733. The funders provided advice about potential study participants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.