Dataset Information

A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne pathogens in reservoir populations.

ABSTRACT:

Background

In natural populations, individuals are infected more often by several pathogens than by just one. In such a context, pathogens can interact. This interaction could modify the probability of infection by subsequent pathogens. Identifying when pathogen associations correspond to biological interactions is a challenge in cross-sectional studies where the sequence of infection cannot be demonstrated.

Methodology/principal findings

Here we modelled the probability of an individual being infected by one and then another pathogen, using a probabilistic model and maximum likelihood statistics. Our model was developed to apply to cross-sectional data, vector-borne and persistent pathogens, and to take into account confounding factors. Our modelling approach was more powerful than the commonly used Chi-square test of independence. Our model was applied to detect potential interaction between Borrelia afzelii and Bartonella spp. that infected a bank vole population at 11% and 57% respectively. No interaction was identified.

Conclusions/significance

The modelling approach we proposed is powerful and can identify the direction of potential interaction. Such an approach can be adapted to other types of pathogens, such as non-persistents. The model can be used to identify when co-occurrence patterns correspond to pathogen interactions, which will contribute to understanding how organism communities are assembled and structured. In the long term, the model's capacity to better identify pathogen interactions will improve understanding of infectious risk.

SUBMITTER: Vaumourin E

PROVIDER: S-EPMC3688727 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne pathogens in reservoir populations.

Vaumourin Elise E Gasqui Patrick P Buffet Jean-Philippe JP Chapuis Jean-Louis JL Pisanu Benoît B Ferquel Elisabeth E Vayssier-Taussat Muriel M Vourc'h Gwenaël G

PloS one 20130620 6

<h4>Background</h4>In natural populations, individuals are infected more often by several pathogens than by just one. In such a context, pathogens can interact. This interaction could modify the probability of infection by subsequent pathogens. Identifying when pathogen associations correspond to biological interactions is a challenge in cross-sectional studies where the sequence of infection cannot be demonstrated.<h4>Methodology/principal findings</h4>Here we modelled the probability of an ind ...[more]

PMID: 23840418

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Project description:BackgroundThe main objective of this study was to determine the prevalence of nine vector-borne pathogens or pathogen genera in roe deer (Capreolus capreolus) in the Netherlands, and to identify which host variables predict vector-borne pathogen presence in roe deer. The host variables examined were the four host factors 'age category', 'sex', 'nutritional condition' and 'health status', as well as 'roe deer density'.MethodsFrom December 2009 to September 2010, blood samples of 461 roe deer were collected and analysed by polymerase chain reaction (PCR) for the presence of genetic material from Anaplasma phagocytophilum, Bartonella spp., Babesia spp., Borrelia burgdorferi sensu lato (s.l.), Borrelia miyamotoi, Neoehrlichia mikurensis, Rickettsia spp., and epizootic haemorrhagic disease virus (EHDV), and by commercial enzyme-linked immunosorbent assay (ELISA) for antibodies against bluetongue virus (BTV). The possible associations of host factors and density with pathogen prevalence and co-infection, and in the case of A. phagocytophilum with bacterial load, were assessed using generalized linear modelling.Results and conclusionAnalysis revealed the following prevalence in roe deer: A. phagocytophilum 77.9%, Bartonella spp. 77.7%, Babesia spp. 17.4%, Rickettsia spp. 3.3%, B. burgdorferi sensu lato 0.2%. Various co-infections were found, of which A. phagocytophilum and Bartonella spp. (49.7% of infected roe deer) and A. phagocytophilum, Bartonella spp. and Babesia spp. (12.2% of infected roe deer) were the most common. Anaplasma phagocytophilum, Babesia spp., and co-infection prevalence were significantly higher in calves than in adult roe deer, whereas the prevalence of Bartonella spp. was lower in roe deer in good nutritional condition than in deer in poor nutritional condition. Local roe deer density was not associated with pathogen presence. The high prevalence of A. phagocytophilum, Bartonella spp., and Babesia spp. is evidence for the role of roe deer as reservoirs for these pathogens. Additionally, the results suggest a supportive role of roe deer in the life-cycle of Rickettsia spp. in the Netherlands.

Dataset Information

A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne pathogens in reservoir populations.

Background

Methodology/principal findings

Conclusions/significance

Publications

A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne pathogens in reservoir populations.

Similar Datasets

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