Spatial patterns of pathogen prevalence in questing Ixodes ricinus nymphs in southern Scandinavia, 2016

Abstract Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

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Predicting the spatial abundance of Ixodes ricinus ticks in southern Scandinavia using environmental and climatic data

Scientific Reports ◽

10.1038/s41598-019-54496-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Lene Jung Kjær ◽

Arnulf Soleng ◽

Kristin Skarsfjord Edgar ◽

Heidi Elisabeth H. Lindstedt ◽

Katrine Mørk Paulsen ◽

...

Keyword(s):

Ixodes Ricinus ◽

Adult Tick ◽

Machine Learning Algorithms ◽

Climatic Data ◽

Tick Abundance ◽

Incidence Data ◽

Southern Scandinavia ◽

Ixodes Ricinus Ticks ◽

Risk Areas ◽

Abundance And Distribution

AbstractRecently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tick-borne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used field data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly influenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a first step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

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Competitive Caching with Machine Learned Advice

Journal of the ACM ◽

10.1145/3447579 ◽

2021 ◽

Vol 68 (4) ◽

pp. 1-25

Author(s):

Thodoris Lykouris ◽

Sergei Vassilvitskii

Keyword(s):

Online Algorithms ◽

Empirical Evaluation ◽

Optimal Solution ◽

Poor Performance ◽

Machine Learning Algorithms ◽

Average Error ◽

Generalization Error ◽

Worst Case ◽

Future Events ◽

Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

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A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

10.1101/796854 ◽

2019 ◽

Author(s):

Swapnali Karvekar ◽

Masoud Abdollahi ◽

Ehsan Rashedi

Keyword(s):

Perceived Exertion ◽

Poor Performance ◽

Machine Learning Algorithms ◽

Motion Sensors ◽

Motion Data ◽

Work Related ◽

Proactive Approach ◽

Fatiguing Exercise ◽

Four Levels ◽

Work Related Musculoskeletal Disorder

AbstractThe fatigue due to repetitive and physically challenging jobs may result in workers’ poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects’ fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue (≥17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals’ gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators’ fatigue level, which may subsequently increase the workers’ performance and reduce the risk of WMSDs.

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Exploring Spatial Patterns of Property Crime Risks in Changchun, China

International Journal of Applied Geospatial Research ◽

10.4018/jagr.2013070105 ◽

2013 ◽

Vol 4 (3) ◽

pp. 80-100 ◽

Cited By ~ 8

Author(s):

Wei Song ◽

Daqian Liu

Keyword(s):

Spatial Patterns ◽

Hot Spots ◽

Property Crime ◽

Ordinary Least Squares ◽

Neighborhood Characteristics ◽

Risk Areas ◽

Gwr Model ◽

The Relationship ◽

The City ◽

Crime Risks

Urban crime has increasingly become a major issue for Chinese cities. Using crime data collected at police precincts in 2008, the main aim of this research is to examine the spatial distribution of property crime which accounted for almost 82% of all crimes in the city of Changchun, and analyze the relationship between the spatial patterns of property crime and neighborhood characteristics. Standardized property crime rates (SCR) were applied to assess the relative risk of property crime across the city. Statistically significant clusters of high-risk areas or hot-spots were detected. A global ordinary least squares (OLS) regression model and a geographically weighted regression (GWR) model were calibrated to explore the risk of property crime as a function of contextual neighborhood characteristics. The analytical results show that significant local variations exist in the relationship between the risk of property crime and several neighborhood socioeconomic variables.

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Spatial Patterns of Microsclerotia of Verticillium dahliae in Soil and Verticillium Wilt of Cauliflower

Phytopathology ◽

10.1094/phyto.1997.87.3.325 ◽

1997 ◽

Vol 87 (3) ◽

pp. 325-331 ◽

Cited By ~ 40

Author(s):

C. L. Xiao ◽

J. J. Hao ◽

K. V. Subbarao

Keyword(s):

Spatial Patterns ◽

Verticillium Wilt ◽

Verticillium Dahliae ◽

Binomial Distribution ◽

Disease Incidence ◽

Soil Samples ◽

Random Pattern ◽

Parameter Estimates ◽

Regular Pattern ◽

Vascular Discoloration

The spatial patterns of microsclerotia of Verticillium dahliae in soil and wilt symptoms on cauliflower were determined at three sites in each of two fields in 1994 and 1995. Each site was an 8 × 8 grid divided into 64 contiguous quadrats (2 by 2 m each). Soil samples were collected to a depth of 15 cm with a probe (2.5 cm in diameter), and samples from four sites in each quadrat were bulked. Plants in each quadrat were cut transversely, and the number of plants with vascular discoloration and the number without discoloration were recorded. The soil was assayed for microsclerotia by the modified Anderson sampler technique. Lloyd's index of patchiness (LIP) was used as an indicator to evaluate the aggregation of microsclerotia in the field. Spatial autocorrelation and geostatistical analyses were also used to assess the autocorrelation of microsclerotia among quadrats. The LIP for microsclerotia was greater than 1, indicating aggregation of propagules; however, the degree of aggregation at most sites was not high. Significant autocorrelation within or across rows was detected in some spatial autocorrelograms of propagules, and anisotropic patterns were also detected in some oriented semivariograms from geostatistical analyses for microsclerotia, indicating the influence of bed preparation in the fields on pathogen distribution. The parameter estimates p and θ in the beta-binomial distribution and the index of dispersion (D) associated with the distribution were used to assess the aggregation of diseased plants at each site. A random pattern of wilt incidence was detected at 7 of 12 sites, and an aggregated pattern was detected at 5 of 12 sites. The degree of aggregation was not high. A regular pattern of wilt severity was detected at all sites. The high disease incidence (77 to 98%) observed at 11 of the 12 sites could be explained by high inoculum density.

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Mapping (un)certainty of machine learning-based spatial prediction models based on predictor space distances

10.5194/egusphere-egu2020-8492 ◽

2020 ◽

Author(s):

Hanna Meyer ◽

Edzer Pebesma

Keyword(s):

Machine Learning ◽

Spatial Patterns ◽

Environmental Science ◽

Prediction Models ◽

Learning Algorithms ◽

Predictor Variable ◽

Spatial Prediction ◽

Machine Learning Algorithms ◽

Training Data ◽

Field Samples

Spatial mapping is an important task in environmental science to reveal spatial patterns and changes of the environment. In this context predictive modelling using flexible machine learning algorithms has become very popular. However, looking at the diversity of modelled (global) maps of environmental variables, there might be increasingly the impression that machine learning is a magic tool to map everything. Recently, the reliability of such maps have been increasingly questioned, calling for a reliable quantification of uncertainties.Though spatial (cross-)validation allows giving a general error estimate for the predictions, models are usually applied to make predictions for a much larger area or might even be transferred to make predictions for an area where they were not trained on. But by making predictions on heterogeneous landscapes, there will be areas that feature environmental properties that have not been observed in the training data and hence not learned by the algorithm. This is problematic as most machine learning algorithms are weak in extrapolations and can only make reliable predictions for environments with conditions the model has knowledge about. Hence predictions for environmental conditions that differ significantly from the training data have to be considered as uncertain.To approach this problem, we suggest a measure of uncertainty that allows identifying locations where predictions should be regarded with care. The proposed uncertainty measure is based on distances to the training data in the multidimensional predictor variable space. However, distances are not equally relevant within the feature space but some variables are more important than others in the machine learning model and hence are mainly responsible for prediction patterns. Therefore, we weight the distances by the model-derived importance of the predictors.&#160;As a case study we use a simulated area-wide response variable for Europe, bio-climatic variables as predictors, as well as simulated field samples. Random Forest is applied as algorithm to predict the simulated response. The model is then used to make predictions for entire Europe. We then calculate the corresponding uncertainty and compare it to the area-wide true prediction error.&#160;The results show that the uncertainty map reflects the patterns in the true error very well and considerably outperforms ensemble-based standard deviations of predictions as indicator for uncertainty.The resulting map of uncertainty gives valuable insights into spatial patterns of prediction uncertainty which is important when the predictions are used as a baseline for decision making or subsequent environmental modelling. Hence, we suggest that a map of distance-based uncertainty should be given in addition to prediction maps.

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Detection of Anaplasma phagocytophilum, Babesia microti, Borrelia burgdorferi, Borrelia miyamotoi, and Powassan Virus in Ticks by a Multiplex Real-Time Reverse Transcription-PCR Assay

mSphere ◽

10.1128/msphere.00151-17 ◽

2017 ◽

Vol 2 (2) ◽

Cited By ~ 16

Author(s):

Rafal Tokarz ◽

Teresa Tagliafierro ◽

D. Moses Cucura ◽

Ilia Rochlin ◽

Stephen Sameroff ◽

...

Keyword(s):

High Frequency ◽

Suffolk County ◽

Babesia Microti ◽

Borrelia Miyamotoi ◽

Pcr Assay ◽

Content Type ◽

Powassan Virus ◽

Pathogen Prevalence ◽

Tick Borne Disease

ABSTRACT The understanding of pathogen prevalence is an important factor in the determination of human risks for tick-borne diseases and can help guide diagnosis and treatment. The implementation of our assay addresses a critical need in surveillance of tick-borne diseases, through generation of a comprehensive assessment of pathogen prevalence in I. scapularis. Our finding of a high frequency of ticks infected with Babesia microti in Suffolk County, NY, implicates this agent as a probable frequent cause of non-Lyme tick-borne disease in this area. Ixodes scapularis ticks are implicated in transmission of Anaplasma phagocytophilum, Borrelia burgdorferi, Borrelia miyamotoi, Babesia microti, and Powassan virus. We describe the establishment and implementation of the first multiplex real-time PCR assay with the capability to simultaneously detect and differentiate all five pathogens in a single reaction. The application of this assay for analysis of ticks at sites in New York and Connecticut revealed a high prevalence of B. microti in ticks from Suffolk County, NY. These findings are consistent with reports of a higher incidence of babesiosis from clinicians managing the care of patients with tick-borne diseases in this region. IMPORTANCE The understanding of pathogen prevalence is an important factor in the determination of human risks for tick-borne diseases and can help guide diagnosis and treatment. The implementation of our assay addresses a critical need in surveillance of tick-borne diseases, through generation of a comprehensive assessment of pathogen prevalence in I. scapularis. Our finding of a high frequency of ticks infected with Babesia microti in Suffolk County, NY, implicates this agent as a probable frequent cause of non-Lyme tick-borne disease in this area.

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Epidemiological Surveillance of Lyme Borreliosis in Bavaria, Germany, 2013–2020

Microorganisms ◽

10.3390/microorganisms9091872 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1872

Author(s):

Merle Margarete Böhmer ◽

Katharina Ens ◽

Stefanie Böhm ◽

Susanne Heinzinger ◽

Volker Fingerle

Keyword(s):

High Risk ◽

Lyme Borreliosis ◽

Severe Disease ◽

Erythema Migrans ◽

Lyme Arthritis ◽

Epidemiological Surveillance ◽

Mandatory Notification ◽

Risk Areas ◽

Tick Borne Disease ◽

Old Females

Lyme borreliosis (LB) is the most common tick-borne disease in Germany. Mandatory notification of acute LB manifestations (erythema migrans (EM), neuroborreliosis (NB), and Lyme arthritis (LA)) was implemented in Bavaria on 1 March 2013. We aimed to describe the epidemiological situation and to identify LB risk areas and populations. Therefore, we analyzed LB cases notified from March 2013 to December 2020 and calculated incidence (cases/100,000 inhabitants) by time, place, and person. Overall, 35,458 cases were reported during the study period (EM: 96.7%; NB: 1.7%; LA: 1.8%). The average incidence was 34.3/100,000, but annual incidence varied substantially (2015: 23.2; 2020: 47.4). Marked regional differences at the district level were observed (annual average incidence range: 4–154/100,000). The Bavarian Forest and parts of Franconia were identified as high-risk regions. Additionally, high risk for LB was found in 5–9-year-old males and in 60–69-year-old females. The first group also had the highest risk of a severe disease course. We were able to identify areas and populations in Bavaria with an increased LB risk, thereby providing a basis for targeted measures to prevent LB. Since LB vaccination is currently not available, such measures should comprise (i) avoiding tick bites, (ii) removing ticks rapidly after a bite, and (iii) treating LB early/adequately.

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Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124204 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4204 ◽

Cited By ~ 8

Author(s):

Abolfazl Mollalo ◽

Kiara M. Rivera ◽

Behzad Vahedi

Keyword(s):

Neural Network ◽

United States ◽

Disease Incidence ◽

Ground Truth ◽

The United States ◽

Incidence Rates ◽

Machine Learning Algorithms ◽

Neural Network Modeling ◽

Public Health Decision ◽

Explanatory Variables

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.

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