A survey was conducted to determine the human tick-borne bacterial infections in the nymphs which were collected from Western Ghats’ fringe forest areas. Tick nymphs were collected using the flagging method from the villages where cases Kyasanur Forest Disease (KFD) were previously reported in the states of Goa, Karnataka and Maharashtra. A total of 200 tick pools consisting of 4587 nymphs were tested by PCR for the detection of bacteria of public health importance, such as Coxiella burnetii and Rickettsia spp. Of these, four pools (4.8%) in Karnataka and three pools (4.4%) in Maharashtra were positive for Coxiella burnetii, while none of the samples from Goa state were positive. Rickettsia spp. were positively obtained from Maharashtra (51.5%), Goa (35.42%) and Karnataka (26.19%). The sequence results of Rickettsia spp. showed similarity to the spotted fever group Candidatus Rickettsia shennongii, Rickettsia conorii subsp. heilongjiangensis and Rickettsia spp. strain koreansis. Individuals are entering into the forest areas for various reasons are more likely to infect with Coxiella burnetii. and Rickettsia spp.

Ticks and tick-borne diseases are an important public health problem worldwide. In India, prevalence of many tick-borne pathogens such as Ganjam virus, Kyasanur forest disease virus (KFDV), Crimean Congo hemorrhagic fever (CCHF) virus and Rickettsia conorii etc., have been documented. The species composition of ticks infesting domestic animals in the Sirumalai area of the Eastern Ghats in Tamil Nadu, South India has been recorded. A total of 2851 tick specimens were collected, and taxonomical identification revealed that 14 species belong to four genera, viz., Haemaphysalis, Rhipicephalus, Amblyomma and Hyalomma. Among the species, Haemaphysalis intermedia from goats and Rhipicephalus microplus from cattle account for 72.6% and 15.2%, respectively. Amblyomma integrum was collected only from cattle (n=22). Rhipicephalus simus, Rhipicephalus bursa, Hyalomma hussaini and Hyalomma kumari have been recorded for the first time this area. The preliminary results confirms the occurrence of rich diversity of tick fauna in this area and recommend further studies to determine the role of these fauna and their public health relevance to livestock and residents.

Background: Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information on the current prevalence of tick vectors is not available in all states of India. Many studies have been carried out utilizing modelling methods to predict the distribution of tick species in other countries. The MaxEnt model is widely used for predicting tick species distribution using bioclimatic variables. Lyme disease vectors such as Ixodes sp., Amblyomma sp., and Dermacentor sp. are the most commonly predicted tick species. However, very few studies have been carried out to predict the distribution of tick species in India. Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. Rhipicephalus (Boophilus) microplus was predicted across India using the generalized linear model (GLM). Identifying the tick vectors in transmitting the infection through conventional survey and identification methods is cumbersome due to the less number of experienced persons available. Prediction of tick vectors of public health concern, including other tick species in different geographical regions of Tamil Nadu, India, is essential for the prevention and control of tick-borne disease in humans and domestic animals. The present study adopts the package ‘SSDM’ (stacked species distribution models) with R software containing ensemble species distribution models to predict the distribution of tick species using different available environmental and climatic data. Results: The categorical variables such as land use and land cover (LULC), soil type, elevation, Bio1, Bio10, Bio15, Bio19 and Bio8 contributed more to modelling the distribution of tick species. MaxEnt, GLM, GBM and GAM are suitable models for predicting the tick species distribution in the present study. Among these models, MaxEnt is the most suitable model for predicting tick species distribution in Tamil Nadu, India. Conclusions: Our results suggest that MaxEnt is a suitable model for predicting the distribution of tick species. Both environmental factors such as LULC, elevation and soil type and bioclimatic factors such as temperature and precipitation contribute significantly to predicting tick species distribution in domestic animals in Tamil Nadu. The SSDM package is very useful and user-friendly graphical user interface for modelling the distribution of tick species. However, the package can be further improved by using higher resolution raster variables in larger areas, which is not currently supported. The predicted elevation range of Ha. spinigera distribution could not be provided due to software limitations.