Pattern of tick aggregation on mice: larger than expected distribution tail enhances the spread of tick-borne pathogens

TL;DR

This study analyzes tick aggregation patterns on mice over nine years, finding that a power-law distribution better describes tick load tails, which influences pathogen spread dynamics and epidemic thresholds.

Contribution

It demonstrates that tick aggregation follows a power-law distribution and shows how seasonal variation affects disease transmission risk, advancing understanding of tick-borne pathogen spread.

Findings

Power-law distribution fits tick load tails better than other models.

Seasonal host abundance alters the tail of tick distribution.

Epidemic thresholds are lower with seasonal variation and power-law distribution.

Abstract

The spread of tick-borne pathogens represents an important threat to human and animal health in many parts of Eurasia. Here, we analysed a 9-year time series of Ixodes ricinus ticks feeding on Apodemus flavicollis mice (main reservoir-competent host for tick-borne encephalitis, TBE) sampled in Trentino (Northern Italy). The tail of the distribution of the number of ticks per host was fitted by three theoretical distributions: Negative Binomial (NB), Poisson-LogNormal (PoiLN), and Power-Law (PL). The fit with theoretical distributions indicated that the tail of the tick infestation pattern on mice is better described by the PL distribution. Moreover, we found that the tail of the distribution significantly changes with seasonal variations in host abundance. In order to investigate the effect of different tails of tick distribution on the invasion of a non-systemically transmitted pathogen, we simulated the transmission of a TBE-like virus between susceptible and infective ticks using a stochastic model. Model simulations indicated different outcomes of disease spreading when considering different distribution laws of ticks among hosts. Specifically, we found that the epidemic threshold and the prevalence equilibria obtained in epidemiological simulations with PL distribution are a good approximation of those observed in simulations feed by the empirical distribution. Moreover, we also found that the epidemic threshold for disease invasion was lower when considering the seasonal variation of tick aggregation.