Analysis of environmental factors influencing lumpy skin disease outbreak seasonality and assessment of its spread risk in the Saratovskaya oblast of Russia

Background and Aim

Lumpy skin disease (LSD) is a transboundary viral disease of cattle that causes serious economic losses due to a significant decrease in meat and milk productivity. This study analyzed the influence of natural and anthropogenic environmental factors on LSD spread seasonality and assessed the risk of LSD outbreaks in the Saratovskaya oblast of the Russian Federation.

Materials and Methods

Data on LSD outbreaks and environmental factors during different seasons were collected for the period 2011-2020 in the Balkan Peninsula, Middle East, and Russia. Risk assessment was performed using mathematical modeling with generalized linear regression and maximum entropy.

Results

Fourteen statistically significant environmental factors influencing LSD spread were identified. The analysis of MaxEnt models built using the selected factors showed that the presence of the pathogen is mostly exerted by: the density of susceptible cattle (an increased risk is observed at a density above 10 and 20 heads/10 km2 in winter and autumn, with a permanent risk in spring and summer), the density of water bodies (the risk is increased at any density in winter and autumn, in the range of 13-23.5 m2/km2 in spring, in the ranges of 0-8 and over 14.5 m2/km2 in summer), and average monthly precipitation rate (the most risky are 105-185 mm/month in winter, 35 mm in spring, 15-105 mm in summer, and above 50 mm in autumn).

Conclusion

LSD tends to spread during the warm season. Compared with other test zones, the Saratovskaya oblast has a negligible risk of disease spread (in winter), low risk (in spring), or medium risk (in summer and autumn). The annual risk is low to medium.

Can Non-Economic Incentives Improve the Incentive Effect of Relocating Immigrants? Evidence from Wuwei City, Located at the Northern Foothills of the Qilian Mountains

Towards the long-term sustainable development of the northern foothills of the Qilian Mountains, the design of migration motivation mechanisms is a key issue. Considering motivation perspectives and social exchange theories, a framework and measurement scale were constructed to analyze the influence of government incentives on Wuwei's relocated immigrants. ANOVA and generalized linear regression models were employed to analyze the differences among regions and groups, along with the influence of various incentives. The results of the study indicate: (1) In Wuwei City, non-economic incentive factors were rated higher than economic incentives. The overall evaluation value for the negative behavior of immigrants was 0.369, and the incentive effect score was 0.633, with significant regional variation. (2) Based on cultural types, Wuwei City was found to have a high percentage of hierarchists, egalitarians and individualists, comprising 39.22%, 23.85% and 21.54%, respectively. Among cultural types, motivation factors and the incentive effect of the government made no difference except for the index of communication and opportunistic behavior. (3) Immigrants' negative behavior was significantly affected by their cultural types. An economic incentive from the government increased the likelihood that immigrants would adopt negative behavior, whereas a participation incentive effectively eliminated those behaviors. (4) The government's economic incentives appear significantly better at promoting the incentive effect than non-economic incentives, but in dispelling the negative behavior of immigrants, non-economic incentives have played a significantly greater role than economic incentives. Incentives associated with relocation provide a new objective basis for determining a policy scheme for the relocation of migrants in the Qilian Mountains, and departments can formulate corresponding incentive models and support policies accordingly as a consequence.

Principal regression for high dimensional covariance matrices

This manuscript presents an approach to perform generalized linear regression with multiple high dimensional covariance matrices as the outcome. In many areas of study, such as resting-state functional magnetic resonance imaging (fMRI) studies, this type of regression can be utilized to characterize variation in the covariance matrices across units. Model parameters are estimated by maximizing a likelihood formulation of a generalized linear model, conditioning on a well-conditioned linear shrinkage estimator for multiple covariance matrices, where the shrinkage coefficients are proposed to be shared across matrices. Theoretical studies demonstrate that the proposed covariance matrix estimator is optimal achieving the uniformly minimum quadratic loss asymptotically among all linear combinations of the identity matrix and the sample covariance matrix. Under certain regularity conditions, the proposed estimator of the model parameters is consistent. The superior performance of the proposed approach over existing methods is illustrated through simulation studies. Implemented to a resting-state fMRI study acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identified a brain network within which functional connectivity is significantly associated with Apolipoprotein E ε4, a strong genetic marker for Alzheimer's disease.

Connectivity-informed adaptive regularization for generalized outcomes

One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV- individuals.

A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK

Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.

The impact of covariate misclassification using generalized linear regression under covariate-adaptive randomization

Under covariate adaptive randomization, the covariate is tied to both randomization and analysis. Misclassification of such covariate will impact the intended treatment assignment; further, it is unclear what the appropriate analysis strategy should be. We explore the impact of such misclassification on the trial's statistical operating characteristics. Simulation scenarios were created based on the misclassification rate and the covariate effect on the outcome. Models including unadjusted, adjusted for the misclassified, or adjusted for the corrected covariate were compared using logistic regression for a binary outcome and Poisson regression for a count outcome. For the binary outcome using logistic regression, type I error can be maintained in the adjusted model, but the test is conservative using an unadjusted model. Power decreased with both increasing covariate effect on the outcome as well as the misclassification rate. Treatment effect estimates were biased towards the null for both the misclassified and unadjusted models. For the count outcome using a Poisson model, covariate misclassification led to inflated type I error probabilities and reduced power in the misclassified and the unadjusted model. The impact of covariate misclassification under covariate-adaptive randomization differs depending on the underlying distribution of the outcome.

Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models

This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety.