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A Probabilistic Model in Cross-Sectional Studies for Identifying Interactions between Two Persistent Vector-Borne Pathogens in Reservoir Populations

Public Library of Science
Publication Date
DOI: 10.1371/journal.pone.0066167
  • Research Article
  • Biology
  • Population Biology
  • Epidemiology
  • Infectious Disease Epidemiology
  • Population Modeling
  • Mathematics
  • Probability Theory
  • Probability Distribution
  • Statistics
  • Statistical Methods
  • Medicine
  • Infectious Diseases
  • Bacterial Diseases
  • Bartonellosis
  • Borrelia Infection
  • Infectious Disease Modeling
  • Vectors And Hosts
  • Zoonoses
  • Veterinary Science
  • Animal Types
  • Wildlife
  • Biology
  • Mathematics
  • Medicine


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.

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