A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne
Elise Vaumourin1, Patrick Gasqui, Jean-Philippe Buffet
1INRA, UR346 Epidémiologie Animale, Saint Genès Champanelle, France. elise.vaumourin@clermont.inra.fr
Plos One
|July 11, 2013
Summary
This study developed a powerful probabilistic model to detect interactions between multiple pathogens in cross-sectional data. The model found no interaction between Borrelia afzelii and Bartonella spp. in bank voles.
Area of Science:
- Ecology
- Epidemiology
- Mathematical Biology
Background:
- Individuals in natural populations are often infected by multiple pathogens simultaneously.
- Pathogen interactions can influence subsequent infection probabilities, complicating ecological and epidemiological studies.
- Distinguishing true biological interactions from mere co-occurrence in cross-sectional data is challenging.
Purpose of the Study:
- To develop a robust statistical model for identifying pathogen interactions using cross-sectional data.
- To assess the model's power compared to traditional statistical tests.
- To investigate potential interactions between Borrelia afzelii and Bartonella spp. in a bank vole population.
Main Methods:
- A probabilistic model utilizing maximum likelihood statistics was employed.
- The model accounts for confounding factors and is suitable for vector-borne and persistent pathogens.
- The approach was validated against the Chi-square test of independence.
Main Results:
- The developed model demonstrated greater power than the Chi-square test.
- Application to bank vole data (11% Borrelia afzelii, 57% Bartonella spp.) revealed no significant interaction between these pathogens.
- The model successfully identified the direction of potential interactions.
Conclusions:
- The proposed modeling approach is effective for detecting pathogen interactions in cross-sectional studies.
- This method can be adapted for various pathogen types, including non-persistent ones.
- Improved identification of pathogen interactions enhances understanding of community assembly, structure, and infectious disease risk.
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