A Bayesian modelling framework for estimating tick-borne pathogen transmission dynamics at the host-tick interface

Understanding the transmission dynamics of tick-borne pathogens at the host-tick interface is challenged by the presence of multiple pathways for tick infection, including (i) host-to-tick transmission, (ii) tick-to-tick (cofeeding) transmission, and (iii) pre-existing infection through vertical transmission or prior feeding. Assessing parameters governing these pathways is critical for identifying the main transmission drivers and, consequently, key prevention and control points. Here, we developed a Bayesian modelling framework that estimates key parameters describing the probability of each transmission pathway and assesses associated factors, including bird species, tick life stage and engorgement level, by jointly modelling transmission at the host-tick interface using data collected in field studies that sample hosts and their ticks. First, by fitting the model to simulated host-tick infection data, we demonstrated the framework’s ability to recover the parameter values underlying these data. Model performance improved significantly when more information was available on variability in cofeeding probability among individual ticks, highlighting the value of testing all collected ticks and recording their spatial distribution on the host in relation to each other. Second, we fitted the model to field data collected at the bird-tick interface in Northeast France in 2023, focusing on Borrelia garinii, B. valaisiana, and Anaplasma phagocytophilum as case pathogens. For all three pathogens studied, models including cofeeding transmission explained the data significantly better than models that did not. Engorgement level was significantly and positively associated with the probability of bird-to-tick transmission for A. phagocytophilum. Finally, the estimated parameters, such as the probability of A. phagocytophilum infection in birds and the probability of Borrelia or Anaplasma infection in ticks before feeding, were comparable to values from an external dataset, not used for model fitting. Our framework provides a valuable foundation for future research to understand tick-borne pathogen transmission dynamics based on epidemiological and ecological field data collected at the host-tick interface.Author summary: Multiple transmission pathways exist at the tick-host interface, including direct host-to-tick transmission, indirect cofeeding transmission between ticks, and pre-existing infection acquired vertically or during earlier feeding. However, conventional approaches often assume simpler, more direct transmission dynamics, which hampers efforts to understand the transmission dynamics of tick-borne pathogens. We introduce a statistical framework that disentangles these pathways and estimates key parameters using epidemiological and ecological field data collected in field studies that sample ticks and their hosts. Through simulations and fitting a model to field data from birds and ticks for three case pathogens (Borrelia garinii, B. valaisiana, and Anaplasma phagocytophilum), we show that the model can recover true parameter values. Our analyses further indicate that detailed information on tick-to-tick interactions on the host substantially improves the reliability of parameter estimates for a given sample size, providing practical guidance for future field studies applying this framework.