Describing fine spatiotemporal dynamics of rat fleas in an insular ecosystem enlightens abiotic drivers of murine typhus incidence in humans

Abstract

Murine typhus is a flea-borne zoonotic disease that has been recently reported on Reunion Island, an oceanic volcanic island located in the Indian Ocean. Five years of survey implemented by the regional public health services have highlighted a strong temporal and spatial structure of the disease in humans, with cases mainly reported during the humid season and restricted to the dry southern and western portions of the island. We explored the environmental component of this zoonosis in an attempt to decipher the drivers of disease transmission. To do so, we used data from a previously published study (599 small mammals and 175 Xenopsylla fleas from 29 sampling sites) in order to model the spatial distribution of rat fleas throughout the island. In addition, we carried out a longitudinal sampling of rats and their ectoparasites over a 12 months period in six study sites (564 rats and 496 Xenopsylla fleas) in order to model the temporal dynamics of flea infestation of rats. Generalized Linear Models and Support Vector Machine classifiers were developed to model the Xenopsylla Genus Flea Index (GFI) from climatic and environmental variables. Results showed that the spatial distribution and the temporal dynamics of fleas, estimated through the GFI variations, are both strongly controlled by abiotic factors: rainfall, temperature and land cover. The models allowed linking flea abundance trends with murine typhus incidence rates. Flea infestation in rats peaked at the end of the dry season, corresponding to hot and dry conditions, before dropping sharply. This peak of maximal flea abundance preceded the annual peak of human murine typhus cases by a few weeks. Altogether, presented data raise novel questions regarding the ecology of rat fleas while developed models contribute to the design of control measures adapted to each micro region of the island with the aim of lowering the incidence of flea-borne diseases.

Fig 1. Location of the study area.

The map shows the relief of Reunion Island (source: French national geographic institute, BD ALTI–data available under the free license Open license 2.0) as well as sampling sites for each dataset and the location of the weather stations used for modelling.

Fig 2. Dynamics of Xenopsylla genus flea index in six study sites, Reunion Island, 2018–2019.

The GFI, rainfall and temperature are plotted in black, blue and red colors, respectively.

Fig 3. Observed and predicted Xenopsylla Genus Flea Index (GFI) in Reunion Island.

A) Map of predicted GFI based on climatic and environmental variables (source for the administrative limits: French national geographic institute BD TOPO–data available under the free license Open license 2.0) B) Bi-dimensional representation of predicted and observed GFI values. Observed GFI values from dataset 1 and dataset 2 are plotted in orange and blue colors, respectively.

Fig 4. Prediction of the Xenopsylla Genus Flea Index (GFI) according to two variables related to two meteorological variables: the maximum temperature and the maximum rainfall over the last 42 days.

The colors and level lines represent the model predictions. The circles correspond to the observations.

Fig 5. Annual predicted dynamics of the Xenopsylla Genus Flea Index (GFI) according to temperature and rainfall.

The red line represents the modelled GFI from meteorological data collected at Pierrefonds weather station, Reunion Island, 2018. The grey bars correspond to the number of murine typhus cases averaged over the 2011–2019 period.

Fig 6

A) Murine typhus incidence on Reunion Island, 2011–2018 (source for the administrative limits: French national geographic institute BD TOPO–data available under the free license Open license 2.0) B) Bi-dimensional representation of the mean predicted GFI and observed murine typhus incidence at IRIS census level. The black line is the regression line.