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

Unexpected winter questing activity of ticks in the Central Midwestern United States

Unexpected questing activity of ticks was noted during the winter months of January and February in the Central Midwestern states of Kansas, Missouri, Oklahoma, and Arkansas. From nine geographically distinct locations, four species of ticks were collected using the flagging method, of which the lone star tick, Amblyomma americanum, was most abundant, followed by the American dog tick, Dermacentor variabilis, the Gulf coast tick, Amblyomma maculatum, and the Black legged tick, Ixodes scapularis. More A. americanum nymphs were caught questing than male or female adults. The winter activity of these medically important ticks in this region poses concern for public health and offers an insight into future tick activity in light of ongoing climate change. More studies on the seasonality of these tick species, and how different climate parameters affect their seasonal activity in this region are warranted and would be expected to benefit for both human and veterinary medicine.

Diversity and seasonality of host-seeking ticks in a periurban environment in the Central Midwest (USA)

Between March 2014 and February 2017, host-seeking ticks were collected during the late spring and summer months seasonally, and as well as continually through all seasons from several sites in a periurban environment in Pittsburg, Kansas, located in the Central Midwestern United States. All three post-emergent life-stages of Amblyomma americanum, and the adults of three other ticks viz. Dermacentor variabilis, A. maculatum, and Ixodes scapularis were collected using the flagging method, and were taxonomically identified using morphological and molecular methods. A total of 15946 ticks were collected from these sites. A vast majority of the ticks collected over the three-year study period was A. americanum (79.01%). The three other species collected included D. variabilis (13.10%), A. maculatum (7.15%), and Ixodes scapularis (0.73%). More female ticks of each species were collected throughout the study period from all sites, and a unimodal activity period was noted for all four species. The diversity, composition, and phenology of these medically significant tick species are discussed.

Spatio-Temporal Dynamics of Tick-Borne Diseases in North-Central Wisconsin from 2000-2016

Lyme disease is a well-recognized public health problem in the USA, however, other tick-borne diseases also have major public health impacts. Yet, limited research has evaluated changes in the spatial and temporal patterns of non-Lyme tick-borne diseases within endemic regions. Using laboratory data from a large healthcare system in north-central Wisconsin from 2000-2016, we applied a Kulldorf's scan statistic to analyze spatial, temporal and seasonal clusters of laboratory-positive cases of human granulocytic anaplasmosis (HGA), babesiosis, and ehrlichiosis at the county level. Older males were identified as the subpopulation at greatest risk for non-Lyme tick-borne diseases and we observed a statistically significant spatial and temporal clustering of cases (p < 0.05). HGA risk shifted from west to east over time (2000-2016) with a relative risk (RR) ranging from 3.30 to 11.85, whereas babesiosis risk shifted from south to north and west over time (2004-2016) with an RR ranging from 4.33 to 4.81. Our study highlights the occurrence of non-Lyme tick-borne diseases, and identifies at-risk subpopulations and shifting spatial and temporal heterogeneities in disease risk. Our findings can be used by healthcare providers and public health practitioners to increase public awareness and improve case detection.

Construction of polluted aerosol in accumulation that affects the incidence of lung cancer

Background

This model demonstrated the correlation between lung cancer incidences and the parts of ambient air pollution according to the National Aeronautics and Space Administration (NASA)'s high resolution technology satellites.

Methods

Chemical type of aerosols was investigated by the Aerosol Diagnostics Model such as black carbon, mineral dust, organic carbon, sea-salt and SO4. The model investigated associations between the six year accumulation of each aerosol and lung cancer incidence by Bayesian hierarchical spatio-temporal model. Which also represented integrated geophysical parameters.

Results

In analyses of accumulated chemical aerosol component from 2010 – 2016, the incidence rate ratio (IRR) of patients in 2017 were estimated. We observed a significant increasing risk for organic carbon exposure (IRR 1.021, 95%CI 1.020–1.022), SO₄, (IRR 1.026, 95% CI 1.025–1.028) and dust, (IRR 1.061, 95% CI 1.058–1.064). There was also suggestion of an increased risk with, every 1 ug/m³ increase in organic carbon compound is associated with 21% increased risk of lung cancer, whereas a 26% excess risk of cancer per 1 ug/m³ increase in mean SO₄ and 61% increased risk of lung cancer for dust levels. The other variables were the negative IRR which did not increase the risk of the exposed group.

Conclusion

With our results, this process can determine that organic carbon, SO₄ and dust was significantly associated with the elevated risk of lung cancer.

Potential Spatial Distribution of the Newly Introduced Long-horned Tick, Haemaphysalis longicornis in North America

The North American distributional potential of the recently invaded tick, Haemaphysalis longicornis, was estimated using occurrence data from its geographic range in other parts of the world and relevant climatic data sets. Several hundred candidate models were built using a correlative maximum entropy approach, and best-fitting models were selected based on statistical significance, predictive ability, and complexity. The median of the best-fitting models indicates a broad potential distribution for this species, but restricted to three sectors—the southeastern United States, the Pacific Northwest, and central and southern Mexico.

Current and Future Distribution of the Lone Star Tick, Amblyomma americanum (L.) (Acari: Ixodidae) in North America

Acarological surveys in areas outside the currently believed leading edge of the distribution of lone star ticks (Amblyomma americanum), coupled with recent reports of their identification in previously uninvaded areas in the public health literature, suggest that this species is more broadly distributed in North America than currently understood. Therefore, we evaluated the potential geographic extent under present and future conditions using ecological niche modeling approach based on museum records available for this species at the Walter Reed Biosystematics Unit (WRBU). The median prediction of a best fitting model indicated that lone star ticks are currently likely to be present in broader regions across the Eastern Seaboard as well as in the Upper Midwest, where this species could be expanding its range. Further northward and westward expansion of these ticks can be expected as a result of ongoing climate change, under both low- and high-emissions scenarios.

Large-scale health disparities associated with Lyme disease and human monocytic ehrlichiosis in the United States, 2007-2013

Promoting health equity is a fundamental public health objective, yet health disparities remain largely overlooked in studies of vectorborne diseases, especially those transmitted by ticks. We sought to identify health disparities associated with Lyme disease and human monocytic ehrlichiosis, two of the most pervasive tickborne diseases within the United States. We used general linear mixed models to measure associations between county-level disease incidence and six variables representing racial/ethnic and socioeconomic characteristics of counties (percent white non-Hispanic; percent with a bachelors degree or higher; percent living below the poverty line; percent unemployed; percent of housing units vacant; per capita number of property crimes). Two ecological variables important to tick demography (percent forest cover; density of white-tailed deer) were included in secondary analyses to contextualize findings. Analyses included data from 2,695 counties in 37 states and the District of Columbia during 2007-2013. Each of the six variables was significantly associated with the incidence of one or both diseases, but the direction and magnitude of associations varied by disease. Results suggested that the incidence of Lyme disease was highest in counties with relatively higher proportions of white and more educated persons and lower poverty and crime rates; the incidence of human monocytic ehrlichiosis was highest in counties with relatively higher proportions of white and less educated persons, higher unemployment rates and lower crime rates. The percentage of housing units vacant was a strong positive predictor for both diseases with a magnitude of association comparable to those between incidence and the ecological variables. Our findings indicate that racial/ethnic and socioeconomic disparities in disease incidence appear to be epidemiologically important features of Lyme disease and human monocytic ehrlichiosis in the United States. Steps to mitigate encroachment of wild flora and fauna into areas with vacant housing might be warranted to reduce disease risk.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States

Background

Dogs in the United States are hosts to a diverse range of vector-borne pathogens, several of which are important zoonoses. This paper describes factors deemed to be significantly related to the prevalence of antibodies to Ehrlichia spp. in domestic dogs, including climatic conditions, geographical factors, and societal factors. These factors are used in concert with a spatio-temporal model to construct an annual seroprevalence forecast. The proposed method of forecasting and an assessment of its fidelity are described.

Methods

Approximately twelve million serological test results for canine exposure to Ehrlichia spp. were used in the development of a Bayesian approach to forecast canine infection. Data used were collected on the county level across the contiguous United States from routine veterinary diagnostic tests between 2011–2015. Maps depicting the spatial baseline Ehrlichia spp. prevalence were constructed using Kriging and head-banging smoothing methods. Data were statistically analyzed to identify factors related to antibody prevalence via a Bayesian spatio-temporal conditional autoregressive (CAR) model. Finally, a forecast of future Ehrlichia seroprevalence was constructed based on the proposed model using county-level data on five predictive factors identified at a workshop hosted by the Companion Animal Parasite Council and published in 2014: annual temperature, percentage forest coverage, percentage surface water coverage, population density and median household income. Data were statistically analyzed to identify factors related to disease prevalence via a Bayesian spatio-temporal model. The fitted model and factor extrapolations were then used to forecast the regional seroprevalence for 2016.

Results

The correlation between the observed and model-estimated county-by-county Ehrlichia seroprevalence for the five-year period 2011–2015 is 0.842, demonstrating reasonable model accuracy. The weighted correlation (acknowledging unequal sample sizes) between 2015 observed and forecasted county-by-county Ehrlichia seroprevalence is 0.970, demonstrating that Ehrlichia seroprevalence can be forecasted accurately.

Conclusions

The forecast presented herein can be an a priori alert to veterinarians regarding areas expected to see expansion of Ehrlichia beyond the accepted endemic range, or in some regions a dynamic change from historical average prevalence. Moreover, this forecast could potentially serve as a surveillance tool for human health and prove useful for forecasting other vector-borne diseases.

Heterogeneous Associations of Ecological Attributes with Tick-Borne Rickettsial Pathogens in a Periurban Landscape

The variations in prevalence levels of two tick-borne rickettsial pathogens, Ehrlichia chaffeensis and Ehrlichia Ewingii, in a periurban environment were evaluated along with their ecological determinants. Tick life stage and sex, month of tick collection, landscape fragmentation, and ecological covariates specific to pasture and woodland sites were considered as explanatory covariates. Questing lone star ticks (Amblyomma americanum) were collected by flagging for an hour once every week during mid-April through mid-August in years 2013 and 2014. A total of 4357 adult and nymphal ticks (woodland = 2720 and pasture = 1637) were collected and assessed for pathogen prevalence by molecular methods. Female A. americanum ticks were more infected with E. chaffeensis than males or nymphs in woodland areas [♂ = 6.05%; ♀ = 12.0%; nymphs = 2.09%] and pastures [♂ = 8.05%; ♀ = 12.03%; nymphs = 3.33%], and the prevalence was influenced by edge density in the landscape. Higher E. ewingii infection was noted among female A. americanum ticks within woodland areas [♂ = 1.89%; ♀ = 2.14%; nymphs = 1.57%], but no such difference was evident in pastures [♂ = 1.03%; ♀ = 1.33%; nymphs = 1.12%]. Prevalence of E. ewingii was influenced by edge contrast index, and the percentage of pasture perimeter that was less than 20 meters from woodland areas. This study elucidates the complexity of tick-borne pathogen ecology and points to the need for further studies on the role of reservoir hosts, particularly that played by small vertebrates, which is not fully understood in the region.

Bayesian Space-Time Patterns and Climatic Determinants of Bovine Anaplasmosis

The space-time pattern and environmental drivers (land cover, climate) of bovine anaplasmosis in the Midwestern state of Kansas was retrospectively evaluated using Bayesian hierarchical spatio-temporal models and publicly available, remotely-sensed environmental covariate information. Cases of bovine anaplasmosis positively diagnosed at Kansas State Veterinary Diagnostic Laboratory (n = 478) between years 2005–2013 were used to construct the models, which included random effects for space, time and space-time interaction effects with defined priors, and fixed-effect covariates selected a priori using an univariate screening procedure. The Bayesian posterior median and 95% credible intervals for the space-time interaction term in the best-fitting covariate model indicated a steady progression of bovine anaplasmosis over time and geographic area in the state. Posterior median estimates and 95% credible intervals derived for covariates in the final covariate model indicated land surface temperature (minimum), relative humidity and diurnal temperature range to be important risk factors for bovine anaplasmosis in the study. The model performance measured using the Area Under the Curve (AUC) value indicated a good performance for the covariate model (> 0.7). The relevance of climatological factors for bovine anaplasmosis is discussed.

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Maximum Entropy-Based Ecological Niche Model and Bio-Climatic Determinants of Lone Star Tick (Amblyomma americanum) Niche

The potential distribution of Amblyomma americanum ticks in Kansas was modeled using maximum entropy (MaxEnt) approaches based on museum and field-collected species occurrence data. Various bioclimatic variables were used in the model as potentially influential factors affecting the A. americanum niche. Following reduction of dimensionality among predictor variables using principal components analysis, which revealed that the first two principal axes explain over 87% of the variance, the model indicated that suitable conditions for this medically important tick species cover a larger area in Kansas than currently believed. Soil moisture, temperature, and precipitation were highly correlated with the first two principal components and were influential factors in the A. americanum ecological niche. Assuming that the niche estimated in this study covers the occupied distribution, which needs to be further confirmed by systematic surveys, human exposure to this known disease vector may be considerably under-appreciated in the state.