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

Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa

Ticks and tick-borne diseases (TTBD) are constraints to the development of livestock and induce potential human health problems. The worldwide distribution of ticks is not homogenous. Some places are ecologically suitable for ticks but they are not introduced in these areas yet. The absence or low density of hosts is a factor affecting the dissemination of the parasite. To understand the process of introduction and spread of TTBD in different areas, and forecast their presence, scientists developed different models (e.g., predictive models and explicative models). This study aimed to identify models developed by researchers to analyze the TTBD distribution and to assess the performance of these various models with a meta-analysis. A literature search was implemented with PRISMA protocol in two online databases (Scopus and PubMed). The selected articles were classified according to country, type of models and the objective of the modeling. Sensitivity, specificity and accuracy available data of these models were used to evaluate their performance using a meta-analysis. One hundred studies were identified in which seven tick genera were modeled, with Ixodes the most frequently modeled. Additionally, 13 genera of tick-borne pathogens were also modeled, with Borrelia the most frequently modeled. Twenty-three different models were identified and the most frequently used are the generalized linear model representing 26.67% and the maximum entropy model representing 24.17%. A focus on TTBD modeling in Africa showed that, respectively, genus Rhipicephalus and Theileria parva were the most modeled. A meta-analysis on the quality of 20 models revealed that maximum entropy, linear discriminant analysis, and the ecological niche factor analysis models had, respectively, the highest sensitivity, specificity, and area under the curve effect size among all the selected models. Modeling TTBD is highly relevant for predicting their distribution and preventing their adverse effect on animal and human health and the economy. Related results of such analyses are useful to build prevention and/or control programs by veterinary and public health authorities.

Forest Connectivity, Host Assemblage Characteristics of Local and Neighboring Counties, and Temperature Jointly Shape the Spatial Expansion of Lyme Disease in United States

Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors.

Seroprevalence of Tick-Borne Anaplasma phagocytophilum Infection in Healthy Adult Population and Patients with Acute Undifferentiated Fever from the Yunnan Province of China

Anaplasma phagocytophilum-the causative agent of human granulocytic anaplasmosis (HGA)-is a tick-borne pathogen transmitted by Ixodid ticks infecting wild and domestic mammals as well as humans. Despite the availability of evidence regarding this emerging infection among vectors, host animals, and individuals in China, there is limited knowledge on the prevalence and distribution of A. phagocytophilum in the Yunnan Province. The aim of this study was to assess the seroprevalence of A. phagocytophilum in healthy adults and patients with acute undifferentiated fever from four regions in the Yunnan Province. The enzyme-linked immunosorbent assay and indirect immunofluorescence assay were used to detect immunoglobulin (Ig) G and IgM antibodies against A. phagocytophilum in sera obtained from 1185 healthy blood donors and 245 patients with acute undifferentiated fever, respectively. Demographic variables were assessed as potential risk factors using the chi-squared test. The rates of seropositivity rates were 7.59% and 4.49% in healthy donors and fever patients, respectively. Analysis of risk factors such as gender, age groups, and place of residence showed statistically significant differences. Infections with A. phagocytophilum occur widely among individuals residing in southwestern China. Our results indicate that there is serological evidence of HGA in this population and presence of acute A. phagocytophilum infections in patients with undifferentiated fever in the Yunnan Province.

Genetic risk prediction using a spatial autoregressive model with adaptive lasso

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

Spatially disaggregated population estimates in the absence of national population and housing census data

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

BACKGROUND

The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.

METHODS

We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.

RESULTS

LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71).

CONCLUSIONS

The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Factors associated with Anaplasma spp. seroprevalence among dogs in the United States

Background

Dogs in the United States are hosts to a diverse range of ticks and tick-borne pathogens, including A. phagocytophilum, an important emerging canine and human pathogen. Previously, a Companion Animal Parasite Council (CAPC)-sponsored workshop proposed factors purported to be associated with the infection risk for tick-transmitted pathogens in dogs in the United States, including climate conditions, socioeconomic characteristics, local topography, and vector distribution.

Methods

Approximately four million test results from routine veterinary diagnostic tests from 2011–2013, which were collected on a county level across the contiguous United States, are statistically analyzed with the proposed factors via logistic regression and generalized estimating equations. Spatial prevalence maps of baseline Anaplasma spp. prevalence are constructed from Kriging and head-banging smoothing methods.

Results

All of the examined factors, with the exception of surface water coverage, were significantly associated with Anaplasma spp. prevalence. Overall, Anaplasma spp. prevalence increases with increasing precipitation and forestation coverage and decreases with increasing temperature, population density, relative humidity, and elevation. Interestingly, socioeconomic status and deer/vehicle collisions were positively and negatively correlated with canine Anaplasma seroprevalence, respectively. A spatial map of the canine Anaplasma hazard is an auxiliary product of the analysis. Anaplasma spp. prevalence is highest in New England and the Upper Midwest.

Conclusions

The results from the two posited statistical models (one that contains an endemic areas assumption and one that does not) are in general agreement, with the major difference being that the endemic areas model estimates a larger prevalence in Western Texas, New Mexico, and Colorado. As A. phagocytophilum is zoonotic, the results of this analysis could also help predict areas of high risk for human exposure to this pathogen.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1431-7) contains supplementary material, which is available to authorized users.

Prevalence, Distribution, and Development of an Ecological Niche Model of Dermacentor variabilis Ticks Positive for Rickettsia montanensis

Rickettsia montanensis has long been considered a nonpathogenic member of the spotted fever group rickettsiae. However, the infection potential of R. montanensis is being revisited in light of its recent association with a case of human infection in the United States and the possibility that additional cases may have been misdiagnosed as Rocky Mountain spotted fever. To this end, DNA was extracted from American dog ticks (Dermacentor variabilis) removed from Department of Defense (DoD) personnel and their dependents at DoD medical treatment facilities (MTFs) during 2002-2012 (n = 4792). These 4792 samples were analyzed for the presence of R. montanensis (n =  36; 2.84%) and all vector DNA was confirmed to be of D. variabilis origin using a novel Dermacentor genus-specific quantitative real-time polymerase chain reaction procedure, Derm, and a novel Dermacentor species multilocus sequence typing assay. To assess the risk of R. montanensis infection, the positive and negative samples were geographically mapped utilizing MTF site locations. Tick localities were imported into a geographical information systems (GIS) program, ArcGIS, for mapping and analysis. The ecological niche modeling (ENM) program, Maxent, was used to estimate the probability of tick presence in eastern United States using locations of both R. montanensis-positive and -negative ticks, climate, and elevation data. The ENM for R. montanensis-positive D. variabilis estimated high probabilities of the positive ticks occurring in two main areas, including the northern Midwest and mid-Atlantic portions of the northeastern regions of United States, whereas the R. montanensis-negative D. variabilis tick model showed a wider estimated range. The results suggest that R. montanensis-positive and -negative D. variabilis have different ranges where humans may be at risk and are influenced by similar and different factors.

GIS and Remote Sensing Use in the Exploration of Lyme Disease Epidemiology

Given the relatively recent recognition of Lyme disease (LD) by CDC in 1990 as a nationally notifiable infectious condition, the rise of reported human cases every year argues for a better understanding of its geographic scope. The aim of this inquiry was to explore research conducted on spatiotemporal patterns of Lyme disease in order to identify strategies for implementing vector and reservoir-targeted interventions. The focus of this review is on the use of GIS-based methods to study populations of the reservoir hosts, vectors and humans in addition to the spatiotemporal interactions between these populations. New GIS-based studies are monitoring occurrence at the macro-level, and helping pinpoint areas of occurrence at the micro-level, where spread within populations of reservoir hosts, clusters of infected ticks and tick to human transmission may be better understood.

Looking forward by looking back: using historical calibration to improve forecasts of human disease vector distributions

Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.

Modeling the Present and Future Geographic Distribution of the Lone Star Tick, Amblyomma americanum (Ixodida: Ixodidae), in the Continental United States

The Lone star tick (Amblyomma americanum L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061-2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of A. americanum-transmitted pathogens in areas where the Lone Star tick has recently or may become established.

Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis

Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

Rural residents in China are at increased risk of exposure to tick-borne pathogens Anaplasma phagocytophilum and Ehrlichia chaffeensis

As emerging tick born rickettsial diseases caused by A. phagocytophilum and E. chaffeensis, anaplasmosis and ehrlichiosis have become a serious threat to human and animal health throughout the world. In particular, in China, an unusual transmission of nosocomial cases of human granulocytic anaplasmosis occurred in Anhui Province in 2006 and more recent coinfection case of A. phagocytophilum and E. chaffeensis was documented in Shandong Province. Although the seroprevalence of human granulocytic anaplasmosis (former human granulocytic ehrlichiosis, HGE) has been documented in several studies, these data existed on local investigations, and also little data was reported on the seroprevalence of human monocytic ehrlichiosis (HME) in China. In this cross-sectional epidemiological study, indirect immunofluorescence antibody assay (IFA) proposed by WHO was used to detect A. phagocytophilum and E. chaffeensis IgG antibodies for 7,322 serum samples from agrarian residents from 9 provinces/cities and 819 urban residents from 2 provinces. Our data showed that farmers were at substantially increased risk of exposure. However, even among urban residents, risk was considerable. Seroprevalence of HGA and HME occurred in diverse regions of the country and tended to be the highest in young adults. Many species of ticks were confirmed carrying A. phagocytophilum organisms in China while several kinds of domestic animals including dog, goats, sheep, cattle, horse, wild rabbit, and some small wild rodents were proposed to be the reservoir hosts of A. phagocytophilum. The broad distribution of vector and hosts of the A. phagocytophilum and E. chaffeensis, especially the relationship between the generalized susceptibility of vectors and reservoirs and the severity of the disease's clinical manifestations and the genetic variation of Chinese HGA isolates in China, is urgently needed to be further investigated.

Social-ecological factors determine spatial variation in human incidence of tick-borne ehrlichiosis

The spatial distribution of human cases of tick-borne diseases is probably determined by a combination of biological and socioeconomic factors. A zoonotic tick-borne pathogen, Ehrlichia chaffeensis, is increasing in human incidence in the USA. In this study, the spatial patterns of probable and confirmed E. chaffeensis-associated cases of ehrlichiosis from 2000 to 2011 were investigated at the zip-code level in Missouri. We applied spatial statistics, including global and local regression models, to investigate the biological and socioeconomic factors associated with human incidence. Our analysis confirms that the distribution of ehrlichiosis in Missouri is non-random, with numerous clusters of high incidence. Furthermore, we identified significant, but spatially variable, associations between incidence and both biological and socioeconomic factors, including a positive association with reservoir host density and a negative association with human population density. Improved understanding of local variation in these spatial factors may facilitate targeted interventions by public health authorities.

Assessing the effects of variables and background selection on the capture of the tick climate niche

BACKGROUND

Modelling the environmental niche and spatial distribution of pathogen-transmitting arthropods involves various quality and methodological concerns related to using climate data to capture the environmental niche. This study tested the potential of MODIS remotely sensed and interpolated gridded covariates to estimate the climate niche of the medically important ticks Ixodes ricinus and Hyalomma marginatum. We also assessed model inflation resulting from spatial autocorrelation (SA) and collinearity (CO) of covariates used as time series of data (monthly values of variables), principal components analysis (PCA), and a discrete Fourier transformation. Performance of the models was measured using area under the curve (AUC), autocorrelation by Moran's I, and collinearity by the variance inflation factor (VIF).

RESULTS

The covariate spatial resolution slightly affected the final AUC. Consistently, models for H. marginatum performed better than models for I. ricinus, likely because of a species-derived rather than covariate effect because the former occupies a more limited niche. Monthly series of interpolated climate always better captured the climate niche of the ticks, but the SA was around 2 times higher and the maximum VIF between covariates around 30 times higher in interpolated than in MODIS-derived covariates. Interpolated or remotely sensed monthly series of covariates always had higher SA and CO than their transformations by PCA or Fourier. Regarding the effects of background point selection on AUC, we found that selection based on a set of rules for the distance to the core distribution and the heterogeneity of the landscape influenced model outcomes. The best selection relied on a random selection of points as close as possible to the target organism area of distribution, but effects are variable according to the species modelled.

CONCLUSION

Testing for effects of SA and CO is necessary before incorporating these covariates into algorithms building a climate envelope. Results support a higher SA and CO in an interpolated climate dataset than in remotely sensed covariates. Satellite-derived information has fewer drawbacks compared to interpolated climate for modelling tick relationships with environmental niche. Removal of SA and CO by a harmonic regression seems most promising because it retains both biological and statistical meaning.

Using administrative medical claims data to supplement state disease registry systems for reporting zoonotic infections

OBJECTIVE

To determine what, if any, opportunity exists in using administrative medical claims data for supplemental reporting to the state infectious disease registry system.

MATERIALS AND METHODS

Cases of five tick-borne (Lyme disease (LD), babesiosis, ehrlichiosis, Rocky Mountain spotted fever (RMSF), tularemia) and two mosquito-borne diseases (West Nile virus, La Crosse viral encephalitis) reported to the Tennessee Department of Health during 2000-2009 were selected for study. Similarly, medically diagnosed cases from a Tennessee-based managed care organization (MCO) claims data warehouse were extracted for the same time period. MCO and Tennessee Department of Health incidence rates were compared using a complete randomized block design within a general linear mixed model to measure potential supplemental reporting opportunity.

RESULTS

MCO LD incidence was 7.7 times higher (p<0.001) than that reported to the state, possibly indicating significant under-reporting (∼196 unreported cases per year). MCO data also suggest about 33 cases of RMSF go unreported each year in Tennessee (p<0.001). Three cases of babesiosis were discovered using claims data, a significant finding as this disease was only recently confirmed in Tennessee.

DISCUSSION

Data sharing between MCOs and health departments for vaccine information already exists (eg, the Vaccine Safety Datalink Rapid Cycle Analysis project). There may be a significant opportunity in Tennessee to supplement the current passive infectious disease reporting system with administrative claims data, particularly for LD and RMSF.

CONCLUSIONS

There are limitations with administrative claims data, but health plans may help bridge data gaps and support the federal administration's vision of combining public and private data into one source.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.

Tickborne infections in the southern United States

Established and emerging tickborne infections are significant causes of human illness in the southern United States. Rocky Mountain spotted fever, human monocytic ehrlichiosis, human ewingii ehrlichiosis and tularemia are known pathogens in this geographic distribution. Rickettsia parkeri and novel ehrlichioses are more recently described tickborne infections reviewed in this article. An understanding of the tick vectors, pathogenesis, epidemiology, clinical presentation, diagnosis and treatment is useful for the clinician treating patients potentially infected with any of these pathogens. Prevention measures, the optimal method for removing an attached tick and current and future vaccine development conclude this review.

Determinants of tick-borne encephalitis in counties of southern Germany, 2001-2008

BACKGROUND

Tick-borne encephalitis (TBE) virus can cause severe symptoms in humans. The incidence of this vector-borne pathogen in humans is characterised by spatial and temporal heterogeneity. To explain the variation in reported human TBE cases per county in southern Germany, we designed a time-lagged, spatially-explicit model that incorporates ecological, environmental, and climatic factors.

RESULTS

We fitted a logistic regression model to the annual counts of reported human TBE cases in each of 140 counties over an eight year period. The model controlled for spatial autocorrelation and unexplained temporal variation. The occurrence of human TBE was found to be positively correlated with the proportions of broad-leafed, mixed and coniferous forest cover. An index of forest fragmentation was negatively correlated with TBE incidence, suggesting that infection risk is higher in fragmented landscapes. The results contradict previous evidence regarding the relevance of a specific spring-time temperature regime for TBE epidemiology. Hunting bag data of roe deer (Capreolus capreolus) in the previous year was positively correlated with human TBE incidence, and hunting bag density of red fox (Vulpes vulpes) and red deer (Cervus elaphus) in the previous year were negatively correlated with human TBE incidence.

CONCLUSIONS

Our approach suggests that a combination of landscape and climatic variables as well as host-species dynamics influence TBE infection risk in humans. The model was unable to explain some of the temporal variation, specifically the high counts in 2005 and 2006. Factors such as the exposure of humans to infected ticks and forest rodent population dynamics, for which we have no data, are likely to be explanatory factors. Such information is required to identify the determinants of TBE more reliably. Having records of TBE infection sites at a finer scale would also be necessary.

The landscape genetics of infectious disease emergence and spread

The spread of parasites is inherently a spatial process often embedded in physically complex landscapes. It is therefore not surprising that infectious disease researchers are increasingly taking a landscape genetics perspective to elucidate mechanisms underlying basic ecological processes driving infectious disease dynamics and to understand the linkage between spatially dependent population processes and the geographic distribution of genetic variation within both hosts and parasites. The increasing availability of genetic information on hosts and parasites when coupled to their ecological interactions can lead to insights for predicting patterns of disease emergence, spread and control. Here, we review research progress in this area based on four different motivations for the application of landscape genetics approaches: (i) assessing the spatial organization of genetic variation in parasites as a function of environmental variability, (ii) using host population genetic structure as a means to parameterize ecological dynamics that indirectly influence parasite populations, for example, gene flow and movement pathways across heterogeneous landscapes and the concurrent transport of infectious agents, (iii) elucidating the temporal and spatial scales of disease processes and (iv) reconstructing and understanding infectious disease invasion. Throughout this review, we emphasize that landscape genetic principles are relevant to infection dynamics across a range of scales from within host dynamics to global geographic patterns and that they can also be applied to unconventional 'landscapes' such as heterogeneous contact networks underlying the spread of human and livestock diseases. We conclude by discussing some general considerations and problems for inferring epidemiological processes from genetic data and try to identify possible future directions and applications for this rapidly expanding field.

Bacterial diversity in Amblyomma americanum (Acari: Ixodidae) with a focus on members of the genus Rickettsia

The lone star tick, Amblyomma americanum (Acari: Ixodidae), is commonly reported from people and animals throughout the eastern U.S. and is associated with transmission of a number of emerging diseases. To better define the microbial communities within lone star ticks, 16S rRNA gene based analysis using bacteria-wide primers, followed by sequencing of individual clones (n = 449) was used to identify the most common bacterial operational taxonomic units (OTUs) present within colony-reared and wild A. americanum. The colony-reared ticks contained primarily sequence affiliated with members of the genus Coxiella (89%; 81/91), common endosymbionts of ticks, and Brevibacterium (11%; 10/91). Similarly, analysis of clones from unfed wild lone star ticks revealed that 96.7% (89/92) of all the OTUs identified were affiliated with Coxiella-like endosymbionts, as compared with only 5.1-11.7% (5/98-9/77) of those identified from wild lone star ticks after feeding. In contrast, the proportion of OTUs identified as Rickettsia sp. in wild-caught ticks increased from 2.2% (2/92) before feeding to as high as 46.8% (36/77) after feeding, and all Rickettsia spp. sequences recovered were most similar to those described from the spotted fever group Rickettsia, specifically R. amblyommii and R. massiliae. Additional characterization of the Rickettsiales tick community by polymerase chain reaction, cloning, and sequencing of 17 kDa and gltA genes confirmed these initial findings and suggested that novel Rickettsia spp. are likely present in these ticks. These data provide insight into the overall, as well as the rickettsial community of wild lone star ticks and may ultimately aid in identification of novel pathogens transmitted by A. americanum.

Landscape epidemiology of vector-borne diseases

Landscape epidemiology describes how the temporal dynamics of host, vector, and pathogen populations interact spatially within a permissive environment to enable transmission. The spatially defined focus, or nidus, of transmission may be characterized by vegetation as well as by climate, latitude, elevation, and geology. The ecological complexity, dimensions, and temporal stability of the nidus are determined largely by pathogen natural history and vector bionomics. Host populations, transmission efficiency, and therefore pathogen amplification vary spatially, thereby creating a heterogeneous surface that may be defined by remote sensing and statistical tools. The current review describes the evolution of landscape epidemiology as a science and exemplifies selected aspects by contrasting the ecology of two different recent disease outbreaks in North America caused by West Nile virus, an explosive, highly virulent mosquito-borne virus producing ephemeral nidi, and Borrelia burgdorferi, a slowly amplifying chronic pathogen producing semipermanent nidi.

Natural history of Ehrlichia chaffeensis: vertebrate hosts and tick vectors from the United States and evidence for endemic transmission in other countries

Ehrlichia chaffeensis, an intracellular gram-negative zoonotic bacterium, is the causative agent of human monocytotropic ehrlichiosis (HME). In humans, the disease can range from a mild, non-specific illness with few to no clinical signs to a moderately severe to fatal disease, especially those with compromised immune systems. E. chaffeensis is maintained in a complex cycle involving white-tailed deer (WTD; Odocoileus virginianus) as a primary reservoir and the lone star tick (LST; Amblyomma americanum) as a primary vector. Numerous other species are naturally exposed to E. chaffeensis and disease has been documented in some domestic animals and wildlife including domestic dogs and ring-tailed lemurs. The organism has been found throughout the natural range of the LST and as the tick continues to expand its range, the geographic range of risk for E. chaffeensis infections will likely continue to expand. Recent data have indicated that E. chaffeensis, or a closely related organism, has been found in many species of ticks and vertebrate hosts in numerous countries.

Modeling of spatially referenced environmental and meteorological factors influencing the probability of Listeria species isolation from natural environments

Many pathogens have the ability to survive and multiply in abiotic environments, representing a possible reservoir and source of human and animal exposure. Our objective was to develop a methodological framework to study spatially explicit environmental and meteorological factors affecting the probability of pathogen isolation from a location. Isolation of Listeria spp. from the natural environment was used as a model system. Logistic regression and classification tree methods were applied, and their predictive performances were compared. Analyses revealed that precipitation and occurrence of alternating freezing and thawing temperatures prior to sample collection, loam soil, water storage to a soil depth of 50 cm, slope gradient, and cardinal direction to the north are key predictors for isolation of Listeria spp. from a spatial location. Different combinations of factors affected the probability of isolation of Listeria spp. from the soil, vegetation, and water layers of a location, indicating that the three layers represent different ecological niches for Listeria spp. The predictive power of classification trees was comparable to that of logistic regression. However, the former were easier to interpret, making them more appealing for field applications. Our study demonstrates how the analysis of a pathogen's spatial distribution improves understanding of the predictors of the pathogen's presence in a particular location and could be used to propose novel control strategies to reduce human and animal environmental exposure.

Climate change and infectious diseases in Europe

Concerted action is needed to address public health issues raised by climate change. In this Review we discuss infections acquired through various routes (arthropod vector, rodent, water, food, and air) in view of a changing climate in Europe. Based on an extensive review of published work and expert workshops, we present an assessment of the infectious disease challenges: incidence, prevalence, and distribution are projected to shift in a changing environment. Due to the high level of uncertainty on the rate of climate change and its impact on infectious diseases, we propose to mount a proactive public health response by building an integrated network for environmental and epidemiological data. This network would have the capacity to connect epidemic intelligence and infectious disease surveillance with meteorological, entomological, water quality, remote sensing, and other data, for multivariate analyses and predictions. Insights from these analyses could then guide adaptation strategies and protect population health from impending threats related to climate change.

Ecological niche of the 2003 west nile virus epidemic in the northern great plains of the United States

BACKGROUND

The incidence of West Nile virus (WNv) has remained high in the northern Great Plains compared to the rest of the United States. However, the reasons for the sustained high risk of WNv transmission in this region have not been determined. To assess the environmental drivers of WNv in the northern Great Plains, we analyzed the county-level spatial pattern of human cases during the 2003 epidemic across a seven-state region.

METHODOLOGY/PRINCIPAL FINDINGS

County-level data on WNv cases were examined using spatial cluster analysis, and were used to fit statistical models with weather, climate, and land use variables as predictors. In 2003 there was a single large cluster of elevated WNv risk encompassing North Dakota, South Dakota, and Nebraska along with portions of eastern Montana and Wyoming. The relative risk of WNv remained high within the boundaries of this cluster from 2004-2007. WNv incidence during the 2003 epidemic was found to have a stronger relationship with long-term climate patterns than with annual weather in either 2002 or 2003. WNv incidence increased with mean May-July temperature and had a unimodal relationship with total May-July precipitation. WNv incidence also increased with the percentage of irrigated cropland and with the percentage of the human population living in rural areas.

CONCLUSIONS/SIGNIFICANCE

The spatial pattern of WNv cases during the 2003 epidemic in the northern Great Plains was associated with both climatic gradients and land use patterns. These results were interpreted as evidence that environmental conditions across much of the northern Great Plains create a favorable ecological niche for Culex tarsalis, a particularly efficient vector of WNv. Further research is needed to determine the proximal causes of sustained WNv transmission and to enhance strategies for disease prevention.