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. 2010 Nov 15;5(11):e13965.
doi: 10.1371/journal.pone.0013965.

Prioritizing Emerging Zoonoses in the Netherlands

Free PMC article

Prioritizing Emerging Zoonoses in the Netherlands

Arie H Havelaar et al. PLoS One. .
Free PMC article


Background: To support the development of early warning and surveillance systems of emerging zoonoses, we present a general method to prioritize pathogens using a quantitative, stochastic multi-criteria model, parameterized for the Netherlands.

Methodology/principal findings: A risk score was based on seven criteria, reflecting assessments of the epidemiology and impact of these pathogens on society. Criteria were weighed, based on the preferences of a panel of judges with a background in infectious disease control.

Conclusions/significance: Pathogens with the highest risk for the Netherlands included pathogens in the livestock reservoir with a high actual human disease burden (e.g. Campylobacter spp., Toxoplasma gondii, Coxiella burnetii) or a low current but higher historic burden (e.g. Mycobacterium bovis), rare zoonotic pathogens in domestic animals with severe disease manifestations in humans (e.g. BSE prion, Capnocytophaga canimorsus) as well as arthropod-borne and wildlife associated pathogens which may pose a severe risk in future (e.g. Japanese encephalitis virus and West-Nile virus). These agents are key targets for development of early warning and surveillance.

Conflict of interest statement

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


Figure 1
Figure 1. Flow chart of the pathway from introduction of a zoonotic pathogen to public health impact, represented by 7 criteria (C1–C7) from which the risk to public health of emerging zoonoses was derived.
Figure 2
Figure 2. Example of card of a randomly generated scenario (QJ) used in the panel session to determine the relative weights of criteria.
The numbers 1–7 represent the criteria C1–C7 (for details see Table 1).
Figure 3
Figure 3. Emerging zoonotic pathogens relevant for the Netherlands (x-axis), prioritized according normalized scores (y-axis, means and 90% confidence intervals based on Monte Carlo simulation).
Three groups of statistically different importance were identified by Classification and Regression Tree analysis and are represented by dashed lines. Mean (standard deviation) of the full dataset: 0.423 (0.124). Mean (standard deviation) of the three clusters: 0.577 (0.047); 0.476 (0.044); 0.317 (0.083).
Figure 4
Figure 4. Comparison of normalized scores using preference-based weights and equal weights.
Figure 5
Figure 5. Comparison of ranking using quantitative and semi-quantitative model.

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