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 power

SUBMITTER: Vaumourin E 

PROVIDER: S-EPMC3688727 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature