Single Prediction Model Research Articles

An intelligent composite model incorporating global / regional X-rays and clinical parameters to predict progressive adolescent idiopathic scoliosis curvatures and facilitate population screening

Adolescent idiopathic scoliosis (AIS) affects up to 5% of the population. The efficacy of school-aged screening remains controversial since it is uncertain which curvatures will progress following diagnosis and require treatment. Patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this work was to develop composite machine learning-based prediction model to accurately predict AIS curves at-risk of progression. 1870 AIS patients with remaining growth potential were identified. Curve progression was defined by a Cobb angle increase in the major curve of ≥6° between first visit and skeletal maturity in curves that exceeded 25°. Separate prediction modules were developed for i) clinical data, ii) global/regional spine X-rays, and iii) hand X-rays. The hand X-ray module performed automated image classification and segmentation tasks towards estimation of skeletal maturity and bone mineral density. A late fusion strategy integrated these domains towards the prediction of progressive curves at first clinic visit. Composite model performance was assessed on a validation cohort and achieved an accuracy of 83.2% (79.3-83.6%, 95% confidence interval), sensitivity of 80.9% (78.2-81.9%), specificity of 83.6% (78.8-84.1%) and an AUC of 0.84 (0.81-0.85), outperforming single modality prediction models (AUC 0.65-0.78). The composite prediction model achieved a high degree of accuracy. Upon incorporation into school-aged screening programs, patients at-risk of progression may be prioritized to receive urgent specialist attention, more frequent follow-up, and pre-emptive treatment. Funding from The Society for the Relief of Disabled Children was awarded to GKHS.

Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series

Global urbanization has made the urban rail transit system an essential service for a growing population. To help urban rail transit stations design optimal operational plans, previous studies have devoted extensive efforts to passenger flow forecasting, especially short-term predictions. By considering the complex pattern of passenger flow, previous research investigated the feasibility of machine learning (ML) methods on different data features and found the limited application of a single ML method. Based on the dynamic historical passenger flow data at an urban rail station, this study proposes an ML-fusion strategy to enhance prediction accuracy, including data aggregation, time series forecasting model selection, and fusion model strategy. First, this study aggregates the data into working days, weekends, and hourly time series for single model development. Based on the predictive performance of single model development, this study selects XGBoost, AdaBoost, and LightGBM from the widely used ML-method pool. To overcome prediction errors caused by the discrepancy between characteristics of passenger flow and single prediction models, the proposed ML-fusion model combines single forecasting models with dynamically predicted passenger flow to enhance the accuracy and efficiency of the prediction. Based on the experimental results, the mean absolute error is 1.54, and the regression coefficient is 0.99, which is in close agreement with unity, which validates that the proposed ML-fusion method has displayed superiority over all other single models tested both in accuracy and stability.

A Functional Data Classification Framework Based on Functional Sufficient Dimension Reduction and Model Averaging

With the ever-evolving advancements in data collection and storage technologies, high-frequency data recorded in patterns that change over time have become increasingly common. In many application scenarios for this type of data, functional data classification has emerged as a prominent issue in the field of statistics. In light of this, this paper proposes a functional data classification model based on the functional sufficient dimensionality reduction method and the idea of model averaging. The proposed method utilizes techniques such as Functional Slice Inverse Regression (FSIR) and Functional Average Variance Estimation (FSAVE) to project an infinite-dimensional random function onto a function space spanned by a finite wiki function, ensuring that the original data's effective information for categorical variables is not lost. Additionally, the Bagging algorithm effectively addresses the overfitting and underfitting problems that arise in single predictive models, while employing model averaging instead of model selection to adaptively select the sufficient dimensionality reduction sub-directions. Notably, in the prediction stage, the number and types of base models are flexible. Empirical analysis demonstrates that the proposed method outperforms some comparative methods in terms of prediction accuracy and robustness.

TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning

Multivariate data analysis, as an important research topic in the field of machine learning, focuses on how to utilize the intrinsic connection between feature variables and target variables. However, in the face of complex multivariate prediction environments, existing single prediction models often fail to obtain ideal results. Meanwhile, existing ensemble prediction models are not always adapted to certain complex data. Moreover, the randomness in the clustering process cannot guarantee the clustering accuracy. Therefore, to improve the model’s prediction accuracy and ability to adapt to complex data and reduce the impact of randomness on clustering accuracy, this paper designs a multivariate prediction model utilizing three-way clustering (TWC) and ensemble learning, which is named the TWC-EL model. First, the initial division of the sample set is realized by k-means clustering algorithm, and further the sample set is divided again via the k-means clustering algorithm to solve the problem of clustering accuracy. Then, the results of clustering twice are combined according to the difference in the number of intersection points and the distance from the samples to the center point of each cluster, and the core and fringe regions of each cluster in the initial clustering results are obtained, forming a new TWC method. Next, based on the correlation between the regions, the obtained core and fringe regions are classified into low-correlation, medium-correlation and high-correlation regions, and an ensemble prediction model is designed by combining the advantages of the Elman neural network model, the Extreme Learning Machine (ELM) model and the back propagation neural network (BPNN) model. Finally, the experimental analysis results exhibit that the constructed TWC-EL model is efficient and feasible, and points out the excellent performance compared with the existing prediction models. The validity of the TWC method and the ensemble prediction model in the proposed TWC-EL model are verified by experiments, respectively.

A decomposition and ensemble model based on GWO and Differential Evolution algorithm for PM2.5 concentration forecasting

Accurate and reliable prediction of PM2.5 concentrations is the basis for appropriate warning measures, and a single prediction model is often ineffective. In this paper, we propose a novel decomposition-and-ensemble model to predict the concentration of PM2.5. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM2.5 series, Support Vector Regression (SVR) to predict each Intrinsic Mode Function (IMF), and a hybrid algorithm based on Differential Evolution (DE) and Grey Wolf Optimizer (GWO) to optimize SVR parameters. The proposed prediction model EEMD-SVR-DEGWO is employed to forecast the concentration of PM2.5 in Guangzhou, Wuhan, and Chongqing of China. Compared with six prediction models, the proposed EEMD-SVR-DEGWO is a reliable predictor and has achieved competitive results.

Improved Analog Ensemble Formulation for 3-Hourly Precipitation Forecasts

Abstract Analog ensembles (AnEns) traditionally use a single numerical weather prediction (NWP) model to make a forecast, then search an archive to find a number of past similar forecasts (analogs) from that same model, and finally retrieve the actual observations corresponding to those past forecasts to serve as members of an ensemble forecast. This study investigates new statistical methods to combine analogs into ensemble forecasts and validates them for 3-hourly precipitation over the complex terrain of British Columbia, Canada. Applying the past analog error to the target forecast (instead of using the observations directly) reduces the AnEn dry bias and makes prediction of heavy-precipitation events probabilistically more reliable—typically the most impactful forecasts for society. Two variants of this new technique enable AnEn members to obtain values outside the distribution of the finite archived observational dataset—that is, they are theoretically capable of forecasting record events, whereas traditional analog methods cannot. While both variants similarly improve heavier precipitation events, one variant predicts measurable precipitation more often, which enhances accuracy during winter. A multimodel AnEn further improves predictive skill, albeit at higher computational cost. AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final AnEn prediction system improves the skill and reliability of point forecasts across all precipitation intensities. Significance Statement The analog ensemble (AnEn) technique is a data-driven method that can improve local weather forecasts. It improves raw model forecasts using past similar model predictions and observations, reducing future forecast errors and providing probabilities for a range of possible outcomes. One limitation of AnEns is that they commonly tend to make rare-event (e.g., heavy precipitation) forecasts appear less extreme. Usually, heavier precipitation events have a higher impact on society and the economy. This study introduces two new AnEn techniques that make operational forecasts of both probabilities and most likely amounts more accurate for heavy precipitation.

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree’s minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a ‘Ranker method’ to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model’s performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

An adaptive ensemble learning by opposite multiverse optimizer and its application in fluid identification for unconventional oil reservoirs

Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems.

Application of coupling mechanism and data-driven models in the Hanjiang river basin

Widely confirmed and applied, data-driven models are an important method for watershed runoff predictions. Since decomposition methods such as time series decomposition cannot automatically handle the decomposition process of date changes and less consideration of influencing factors before decomposition, resulting in insufficient correlation analysis between influencing factors and forecast objects, we propose a method based on hydrological model decomposition to generate time series state variables (broadening the range of influencing factors to be considered). In this study, we constructed hydrological models wherein rainfall and other hydrological elements are decomposed into hydrological and hydrodynamic characteristic state variables to expand the range of the prediction factors. A data-driven model was then built to perform runoff predictions in the Han River Basin. The results showed that compared with the single prediction model, the prediction results based on the coupling model were superior, the performance evaluation grade of the coupling model was high, and the coupling model had a higher stability.

Combined Prediction Model of PM2.5 Concentration Based on Wavelet Transform and LSTM

To improve the prediction accuracy of PM2.5 concentration and overcome the applicability limitation of a single prediction method, a combined prediction model of PM2.5 concentration based on wavelet transform and long short-term memory (LSTM) is proposed. The potential relationship between PM2.5 and other air quality indexes is explored by using feature engineering, and strong correlation factors are selected as input vectors. Wavelet transform is used to decompose and refine the time series of related factors at multiple scales. And the LSTM is used to train the time series in different scales, and the final prediction results are generated through reconstruction. The results show that the combined model has a better prediction effect compared with the single prediction model, which improves the prediction accuracy and model generalization ability.

Deep reinforcement learning based spectrum prediction for bursty bands

Spectrum prediction plays an important role for the secondary user (SU) to utilize the shared spectrum resources. However, currently utilized prediction methods are not well applied to spectrum with high burstiness, as parameters of prediction models cannot be adjusted properly. This paper studies the prediction problem of bursty bands. Specifically, we first collect real WiFi transmission data in 2.4GHz Industrial, Scientific, Medical (ISM) band which is considered to have bursty characteristics. Feature analysis of the data indicates that the spectrum occupancy law of the data is time-variant, which suggests that the performance of commonly used single prediction model could be restricted. Considering that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance, we then propose a deep-reinforcement learning based multilayer perceptron (DRL-MLP) method to address this matching problem. The state space of the method is composed of feature vectors, and each of the vectors contains multi-dimensional feature values. Meanwhile, the action space consists of several multilayer perceptrons (MLPs) that are trained on the basis of multiple classified data sets. We finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed method. The results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of the prediction accuracy.

Prediction of Corporate Bankruptcy using Machine Learning

Over the past few decades, corporate bankruptcy has become a source of concern for various business stakeholders such as investors and management. It has also piqued the interest of researchers worldwide. Because there are so many variables that contribute to bankruptcy, it is insufficient to rely solely on a single predictive model; instead, the difficulty is in pinpointing the crucial elements that carry the greatest weight. The significant class imbalance in the data is another significant obstacle that impairs the model's functionality. While many methods, such as Decision Trees, Support Vector Machines, Neural Networks, etc., have been studied before with various pre-processing approaches, we advance this research by applying a novel combination of a Random Forest feature selection technique and SMOTEENN hybrid resampling technique on Polish Bankruptcy dataset. We next apply four classifiers to the altered data: Random Forest, Decision Tree, KNN, and AdaBoost, and assess the effectiveness of each model. As a result of our suggested approach, the Random Forest classifier had the highest accuracy of 89%, while the AdaBoost model as a whole outperformed it with a Recall of 73% and a Geometric Mean of 80%.

Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration

Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combination forecasting typically arbitrarily choose a certain set of single forecasting models. However, a particular selection of forecasting models do not apply to all data sets due to the nonlinearity and nonsmoothness of the carbon trading price series. Therefore, choosing suitable single forecasting models for the combination is crucial. Considering the limitations of the current study, this study constructs a combined carbon trading forecasting model based on Lasso regression and optimal integration. By invoking the Lasso regression model, we can select suitable single forecasting models for combination forecasting based on the variation patterns of different training sets. Meanwhile, ARIMA, NARNN, LSTM, and 11 other single forecasting models are screened in this study, including both traditional statistical forecasting models and artificial intelligence forecasting models. First, the carbon price index is predicted using 11 single prediction models. Furthermore, given the multi-collinearity of the single prediction series, this study employs Lasso regression to reduce the dimensions of the single prediction models, which are then used to construct an optimal combination prediction model. Finally, the proposed model is applied to SZA-2017 and SZA-2019 carbon price data in Shenzhen. The results demonstrate that the model developed in this study outperforms other benchmark prediction models in terms of prediction error and direction accuracy, showing the efficacy of the proposed method.

CapRadar: Real-time adaptive bandwidth prediction for dynamic wireless networks

With the emergence of 4G/5G cellular networks, mobile Internet is becoming increasingly popular and numerous mobile applications have emerged, which puts higher demands on the accuracy of predicting available bandwidth. However, compared to WiFi and wired networks, the links of 4G/5G cellular networks are highly dynamic, and the unstable nature of their network environments makes the bandwidth trajectories vary. Traditional bandwidth prediction models rarely consider the bandwidth characteristics of various network scenarios, and a single prediction model is difficult to be applied to all scenarios, which makes it challenging to achieve high accuracy of available bandwidth prediction. To solve the above problems, we propose a real-time bandwidth prediction method called CapRadar. The method classifies bandwidth into scenarios and matches the optimal prediction model for each type of scenario, i.e., switches the prediction model in real time according to the changes of the scenario. Specifically, we first extract the statistical characteristics of the bandwidth using statistical method, and based on the extracted characteristics, a SVM classifier is used to detect different network scenarios. After that, the best prediction model is matched for them in the algorithm library based on the scenario classification results. The experimental results show that CapRadar can reduce the root mean square error (RMSE) by about 18.9% and the mean error (MAE) by about 21.5%. For practical applications, we use a pre-trained SVM model for real-time scenario detection, and then we can dynamically switch the prediction model.

Neural computing for grey Richards differential equation to forecast traffic parameters with various time granularity

The existing traffic parameter prediction methods generally adopt a single prediction model, but the fusion of different theories and methods can complement each other and improve the prediction performance of the model. Starting from the statistical distribution characteristics of traffic flow, this work introduces the Richards equation to conduct grey modeling, which is used to simulate the development trend of traffic parameters; and then fuses the abilities of error feedback adjustment and complex nonlinear fitting of neural network to estimate the parameters of the grey model and forecast the volatility of traffic flow respectively. At the same time, a dynamic prediction framework of real-time data update is built for the new model, and finally establish the dynamic grey Richards neural network model (DGR-NN).Apply the new model to different traffic parameters (Speed; Volume; Jam mileage) and data resolutions (5 min; 15 min; 1 h), the modeling effect of DGR-NN is significantly improved compared to the grey Richards model (GRM), and the training and testing errors of the model in the three forecast scenarios are reduced to varying degrees, where the testing MAPE, RMSE and STD are reduced by 1.80% ∼ 10.87%, 2.55% ∼ 7.26% and 8.08–25.56% respectively. Furthermore, the results of the new model were verified and analyzed with the other five comparison models, among which the boxplot of APE shows that the error distribution of DGR-NN prediction data is concentrated and the value level is relatively low. It can be seen that DGR-NN can accurately and stably forecast traffic parameters of different time granularities.

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China.

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the "double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

A hybrid WOA-SVM based on CI for improving the accuracy of shopping mall air conditioning system energy consumption prediction

Energy consumption prediction is of great practical significance for the study of energy-saving scheduling of building air-conditioning systems. However, there are two problems in prediction models: (1) A single prediction model is difficult to solve the nonlinear problem of building energy consumption data. (2) A single prediction result can hardly reflect the uncertainty in the building operation process. Therefore, in this study, a hybrid prediction model with an improved whale optimization algorithm- optimization support vector machine (IWOA-SVM) is proposed based on the air conditioning system of a shopping mall in Xi'an. The WOA is improved using the opposition-based learning strategy (OBL), differential evolution algorithm (DE), and introducing a control parameter λ. The Confidence Interval (CI) of the model fitting error is used to represent the prediction results. The results show that the root mean square error (RMSE) of the optimized model is reduced by 9.5, and the (RMSE) of the total energy consumption prediction value after adding CI is >1.68. The method achieves higher model accuracy, reflecting the operational fluctuations of the building.

Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator

Aims: Based on the income data of urban and rural residents in China from 1998 to 2021, the income gap variables of urban and rural residents were constructed, and the combination prediction method was used to predict the income gap between urban and rural residents in China.
 Methodology: Grey prediction model GM (1,1), Holt-winter seasonless exponential smoothing model and autoregressive moving average ARIMA model were used to construct an order weighted arithmetic mean combination model induced by IOWA with the minimum sum of error squares. Then, by building new weights, three individual forecasting models and combination forecasting models are used to forecast the income gap between urban and rural residents in the next five years.
 Results: The results show that the accuracy of the combined prediction model is significantly better than that of the single prediction model.
 Conclusion: It can be known that the income gap between urban and rural residents will widen further in the future, with an average growth rate of 4.55%.

Genetic parameters and multi-trait genomic prediction for hemoparasites infection levels in cattle

Babesiosis and anaplasmosis are tick-borne diseases that substantially affect the economic outcomes of livestock production in tropical countries. This study aimed to evaluate the genetics of resistance to infection caused by these parasites through the estimation of heritabilities and genetic correlations of infection levels among Babesia bigemina (BigIL), B. bovis (BovIL), and Anaplasma marginale (AmIL), and tick counts (TC). The predictive ability of single and multi-trait genomic prediction models was evaluated through various combinations of these traits. To our knowledge, this is the first genomic study to examine BigIL and AmIL. Infection levels of BigIL (n = 1,882), BovIL (n = 1,858), and AmIL (n = 1,523) were estimated from blood samples using real-time PCR. TC phenotypes (n = 5,867) were obtained by counting the number of parasites larger than 4.5 mm from the right-hand side of each animal. Genotypic data were available for 3,977 animals which were then imputed up to ∼777,000 SNP and, after quality control, 502,398 SNP remained for downstream analyses. Variance components for BigIL and AmIL and the genetic correlations between traits were estimated using a Bayesian approach. The single-step best linear unbiased prediction was used to estimate genomic breeding values (GBV). The heritability estimates for BigIL and AmIL were low at 0.094 and 0.090, respectively, suggesting high environmental influence levels for both traits. The genetic correlations between tick count and infection levels for BigIL (0.239), BovIL (0.160), and AmIL (-0.019) were low, as well as the correlation between AmIL and BovIL (0.043). The genetic correlations between BigIL and BovIL (0.524) and BigIL and AmIL (0.793) were high, which contributed to improved GBV accuracies when these traits were combined in multi-trait models in comparison to single-trait models. These results suggested that multi-trait genomic prediction models of infection levels for tick-borne diseases are preferable to single-trait models. Additionally, our results indicated that the TC data and the GBV based on them are not useful for predicting infection levels of BigIL, BovIL, and AmIL.