Data-driven predictions and novel hypotheses about zoonotic tick vectors from the genus Ixodes

Abstract

Abstract Background With the resurgence of tick-borne diseases such as Lyme disease and the emergence of new tick-borne pathogens such as Powassan virus, understanding what distinguishes vectors from non-vectors, and predicting undiscovered tick vectors is a crucial step towards mitigating disease risk in humans. We aimed to identify intrinsic traits that predict which Ixodes tick species are confirmed or strongly suspected to be vectors of zoonotic pathogens. Methods We focused on the well-studied tick genus Ixodes from which many species are known to transmit zoonotic diseases to humans. We apply generalized boosted regression to interrogate over 90 features for over 240 species of Ixodes ticks to learn what intrinsic features distinguish zoonotic vectors from non-vector species. In addition to better understanding the biological underpinnings of tick vectorial capacity, the model generates a per species probability of being a zoonotic vector on the basis of intrinsic biological similarity with known Ixodes vector species. Results Our model predicted vector status with over 91% accuracy, and identified 14 Ixodes species with high probabilities (80%) of transmitting infections from animal hosts to humans on the basis of their traits. Distinguishing characteristics of zoonotic tick vectors of Ixodes tick species include several anatomical structures that influence host seeking behavior and blood-feeding efficiency from a greater diversity of host species compared to non-vectors. Conclusions Overall, these results suggest that zoonotic tick vectors are most likely to be those species where adult females hold a fecundity advantage by producing more eggs per clutch, which develop into larvae that feed on a greater diversity of host species compared to non-vector species. These larvae develop into nymphs whose anatomy are well suited for more efficient and longer feeding times on soft-bodied hosts compared to non-vectors, leading to larger adult females with greater fecundity. In addition to identifying novel, testable hypotheses about intrinsic features driving vectorial capacity across Ixodes tick species, our model identifies particular Ixodes species with the highest probability of carrying zoonotic diseases, offering specific targets for increased zoonotic investigation and surveillance.

Keywords

• Host range
• Machine learning
• Surveillance
• Transmission
• Vectorial capacity
• Hypostome