Dataset Information

Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

ABSTRACT:

Background

Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis, and Anaplasma phagocytophilum, the causative agent of human granulocytotropic anaplasmosis. These approaches extended environmental modelling based on logistic regression by incorporating both spatial autocorrelation (the tendency for pathogen distributions to be clustered in space) and spatial heterogeneity (the potential for environmental relationships to vary spatially).

Results

Incorporating either spatial autocorrelation or spatial heterogeneity resulted in substantial improvements over the standard logistic regression model. For E. chaffeensis, which was common within the boundaries of its geographic range and had a highly clustered distribution, the model based only on spatial autocorrelation was most accurate. For A. phagocytophilum, which has a more complex zoonotic cycle and a comparatively weak spatial pattern, the model that incorporated both spatial autocorrelation and spatially heterogeneous relationships with environmental variables was most accurate.

Conclusion

Spatial autocorrelation can improve the accuracy of predictive disease risk models by incorporating spatial patterns as a proxy for unmeasured environmental variables and spatial processes. Spatial heterogeneity can also improve prediction accuracy by accounting for unique ecological conditions in different regions that affect the relative importance of environmental drivers on disease risk.

SUBMITTER: Wimberly MC

PROVIDER: S-EPMC2373776 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

Wimberly Michael C MC Baer Adam D AD Yabsley Michael J MJ

International journal of health geographics 20080415

<h4>Background</h4>Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human mono ...[more]

PMID: 18412972

Similar Datasets

Project description:Ticks are blood-feeding arthropods and transmit a variety of medically important viral, bacterial, protozoan pathogens to animals and humans. Ticks also harbor a diverse community of microbes linked to their biological processes, such as hematophagy, and hence affect vector competence. The interactions between bacterial and/or protozoan pathogens and the tick microbiome is a black-box, and therefore we tested the hypothesis that the presence of a protozoan or bacterial pathogen will alter the microbial composition within a tick. Hence, this study was designed to define the microbial composition of two tick species, Hyalomma (H.) anatolicum and Rhipicephalus (R.) microplus. We used a combination of PCR based pathogen (Anaplasma marginale and Theileria species) and symbiont (Wolbachia species) identification followed by metagenomic sequencing and comparison of the microbial communities in PCR positive and negative ticks. A total of 1786 operational taxonomic units was identified representing 25 phyla, 50 classes, and 342 genera. The phylum Proteobacteria, Firmicutes, Actinobacteriota, and Bacteroidota were the most represented bacteria group. Alpha and beta diversity were not significantly affected in the presence or absence of Theileria sp. and A. marginale as see with H. anatolicum ticks. Interestingly, bacterial communities were significantly reduced in Theileria sp. infected R. microplus ticks, while also exhibiting a significant reduction in microbial richness and evenness. Putting these observations together, we referred to the effect the presence of Theileria sp. has on R. microplus a "pathogen-induced dysbiosis". We also identify the presence of Plasmodium falciparum, the causative agent of human malaria from the microbiome of both H. anatolicum and R. microplus ticks. These findings support the presence of a "pathogen-induced dysbiosis" within the tick and further validation experiments are required to investigate how they are important in the vector competence of ticks. Understanding the mechanism of "pathogen-induced dysbiosis" on tick microbial composition may aid the discovery of intervention strategies for the control of emerging tick-borne infections.

Project description:BackgroundIxodes ricinus and Ixodes persulcatus are the main vectors of Lyme borreliosis spirochetes and several other zoonotic bacteria in northern Europe and Russia. However, few studies screening bacterial pathogens in Finnish ticks have been conducted. Therefore, reports on the occurrence and prevalence of several bacterial pathogens detected from ticks elsewhere in Europe and Russia are altogether missing from Finland. The main aim of the current study was to produce novel data on the occurrence and prevalence of several tick-borne bacterial pathogens in ticks collected from southwestern Finland.MethodsTicks were collected in 2013-2014 by blanket dragging from 25 localities around southwestern Finland, and additionally from a dog in Lempäälä. Collected ticks were molecularly identified and screened for Borrelia burgdorferi s.l., Borrelia miyamotoi, Rickettsia, Bartonella and Candidatus Neoehrlichia mikurensis using quantitative PCR. Furthermore, detected Rickettsia spp. were sequenced using conventional PCR to determine species.ResultsA total of 3169 ticks in 1174 DNA samples were screened for the listed pathogens. The most common bacteria detected was B. burgdorferi (s.l.) (18.5 % nymphal and 23.5 % adult ticks), followed by Rickettsia spp. (1.1 %; 5.1 %) and B. miyamotoi (0.51 %; 1.02 %). B. miyamotoi and Rickettsia spp. were also detected in larval samples (minimum infection rates 0.31 % and 0.21 %, respectively). Detected Rickettsia spp. were identified by sequencing as R. helvetica and R. monacensis. All screened samples were negative for Bartonella spp. and Ca. N. mikurensis.ConclusionsIn the current study we report for the first time the presence of Rickettsia in Finnish ticks. Furthermore, Rickettsia spp. and B. miyamotoi were found from larval tick samples, emphasizing the importance they may have as vectors of these pathogens. Comparisons of tick density estimates and B. burgdorferi (s.l.) prevalence made between the current study and a previous study conducted in 2000 in ten out of the 25 study localities suggest that an increase in tick abundance and B. burgdorferi (s.l.) prevalence has occurred in at least some of the study localities.

Dataset Information

Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

Background

Results

Conclusion

Publications

Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets