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A bayesian generalized linear model for Crimean-Congo Hemorrhagic Fever Incidents

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2018年第1期23卷 153-170页

作者： Ryu, Duchwan Bilgili, Devrim Ergonul, Onder Liang, Faming Ebrahimi, Nader Northern Illinois Univ Div Stat De Kalb IL 60115 USA Univ North Florida Dept Math & Stat Jacksonville FL 32224 USA Koc Univ Sch Med Infect Dis & Clin Microbiol Dept Istanbul Turkey Koc Univ Sch Med Dept Publ Hlth Istanbul Turkey Univ Florida Dept Biostat Gainesville FL 32611 USA

Global spread of the Crimean-Congo hemorrhagic fever (CCHF) is a fatal viral infection disease found in parts of Africa, Asia, Eastern Europe and Middle East, with a fatality rate of up to 30%. A timely prediction of the prevalence of CCHF incidents is highly desirable, while CCHF incidents often exhibit nonlinearity in both temporal and spatial features. However, the modeling of discrete incidents is not trivial. Moreover, the CCHF incidents are monthly observed in a long period and take a nonlinear pattern over a region at each time point. Hence, the estimation and the data assimilation for incidents require extensive computations. In this paper, using the data augmentation with latent variables, we propose to utilize a dynamically weighted particle filter to take advantage of its population controlling feature in data assimilation. We apply our approach in an analysis of monthly CCHF incidents data collected in Turkey between 2004 and 2012. The results indicate that CCHF incidents are higher at Northern Central Turkey during summer and that some beforehand interventions to stop the propagation are recommendable. Supplementary materials accompanying this paper appear on-line.

关键词： bayesian generalized linear model Data augmentation Dynamically weighted importance sampling Radial basis function networks Spatiotemporal model

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THE EFFECT OF LOGGING ON ANIMAL PHENOTYPES: A CASE STUDY OF FLUCTUATING ASYMMETRY OF GROUND BEETLE NOTIOPHILUS RUFIPES (COLEOPTERA: CARABIDAE)

REPORTS OF FORESTRY RESEARCH-ZPRAVY LESNICKEHO VYZKUMU

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REPORTS OF FORESTRY RESEARCH-ZPRAVY LESNICKEHO VYZKUMU 2024年第3期69卷 179-187页

作者： Stoces, Dominik Sipos, Jan Mendelova Univ Brne Agron Fak Ustav Zool Rybarstvi Hydrobiol Vcelarstvi Zemedelska 1-1665 Brno 61300 Czech Republic

We compared the rate of variability in the size of different body parts among three different treatments created by forest management (i.e. clearing, pole-stage stand, ecotone) in the Podyji National Park (Czech Republic). In order to describe changes, we used bioindicative values of the Carabidae (Coleoptera) with the example of Notiophilus rufipes (Curtis, 1829). We conducted fluctuating asymmetry (FA) of ten important metrical traits (e.g. antennal segments, length and width of third palpomer of the labial palpus). We found that specimens have sex specific association to FA of second antennal segment. The interaction of sex and treatment negatively affects the distribution of FA in polestage stand. We revealed that males had higher level of FA in pole-stage stand than females. Ecotones are zones where individuals from diverse habitats interact. model of FA, with set sex as random effect, have positive association to litter and negative association to Collembola. This research contributes to forest management strategies by demonstrating how human-induced interventions influence eco-evolutionary link between stress and disturbance. The research suggests that for forest management, forest openings in thermophilic oak stands could support phenotypic variation in rare epigeic species.

关键词： environmental stress forest harvesting disturbance indicator ecological niche bayesian generalized linear model

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Diurnal and nocturnal mosquitoes escape looming threats using distinct flight strategies

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CURRENT BIOLOGY 2022年第6期32卷 1232-+页

作者： Cribellier, Antoine Straw, Andrew D. Spitzen, Jeroen Pieters, Remco P. M. van Leeuwen, Johan L. Muijres, Florian T. Wageningen Univ Expt Zool Grp Elst 1 NL-6708 WD Wageningen Netherlands Univ Freiburg Inst Biol 1 Hauptstr 1 D-79104 Freiburg Germany Univ Freiburg Bernstein Ctr Freiburg Hauptstr 1 D-79104 Freiburg Germany Wageningen Univ Lab Entomol Droevendaalsesteeg 1 NL-6708 PB Wageningen Netherlands

Flying insects have evolved the ability to evade looming objects, such as predators and swatting hands. This is particularly relevant for blood-feeding insects, such as mosquitoes that routinely need to evade the defensive actions of their blood hosts. To minimize the chance of being swatted, a mosquito can use two distinct strategies-continuously exhibiting an unpredictable flight path or maximizing its escape maneuverability. We studied how baseline flight unpredictability and escape maneuverability affect the escape performance of day-active and night-active mosquitoes (Aedes aegypti and Anopheles coluzzii, respectively). We used a multi-camera high-speed videography system to track how freely flying mosquitoes respond to an event -triggered rapidly approaching mechanical swatter, in four different light conditions ranging from pitch darkness to overcast daylight. Results show that both species exhibit enhanced escape performance in their natural blood-feeding light condition (daylight for Aedes and dark for Anopheles). To achieve this, they show strikingly different behaviors. The enhanced escape performance of Anopheles at night is explained by their increased baseline unpredictable erratic flight behavior, whereas the increased escape performance of Aedes in overcast daylight is due to their enhanced escape maneuvers. This shows that both day and night-active mosquitoes modify their flight behavior in response to light intensity such that their escape performance is maximum in their natural blood-feeding light conditions, when these defensive actions by their blood hosts occur most. Because Aedes and Anopheles mosquitoes are major vectors of several deadly human diseases, this knowledge can be used to optimize vector control methods for these specific species.

关键词： Aedes aegypti Anopheles coluzzii bayesian generalized linear model evasive maneuvers hidden Markov model insect flight protean insurance real-time three-dimensional videography unpredictable behavior

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Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia (vol 8, 42, 2022)

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PORCINE HEALTH MANAGEMENT 2022年第1期8卷 42页

作者： Ciuoderis-Aponte, Karl Diaz, Andres Muskus, Carlos Pena, Mario Hernandez-Ortiz, Juan Osorio, Jorge Univ Nacl Colombia Consortium Colombia Wisconsin Hlth 1 Sede Medellin Cra75 61-85 Medellin 050034 Colombia Pig Improvement Co Querato Mexico Univ Antioquia Programa Estudio & Control Enfermedades Trop PECE Medellin Colombia Asociac Porkcolombia Fondo Nacl Porcicultura Bogota Colombia Univ Wisconsin Madison Dept Pathobiol Sci Consortium Colombia Wisconsin Hlth 1 Madison WI 53706 USA

Biosecurity protocols (BP) and good management practices are key to reduce the risk of introduction and transmission of infectious diseases into the pig farms. In this observational cross-sectional study, survey data were collected from 176 pig farms with inventories over 100 sows in Colombia. We analyzed a complex survey dataset to explore the structure and identify clustering patterns using Multiple Correspondence Analysis (MCA) of swine farms in Colombia, and estimated its association with Influenza A virus detection. Two principal dimensions contributed to 27.6% of the dataset variation. Farms with highest contribution to dimension 1 were larger farrow-to-finish farms, using self-replacement of gilts and implementing most of the measures evaluated. In contrast, farms with highest contribution to dimension 2 were medium to large farrow-to-finish farms, but implemented biosecurity in a lower degree. Additionally, two farm clusters were identified by Hierarchical Cluster Analysis (HCA), and the odds of influenza A virus detection was statistically different between clusters (OR 7.29, CI: 1.7,66, p = < 0.01). Moreover, after logistic regression analysis, three important variables were associated with higher odds of influenza detection: (1) "location in an area with a high density of pigs", (2) "farm size", and (3) "after cleaning and disinfecting, the facilities are allowed to dry before use". Our results revealed two clustering patterns of swine farms. This systematic analysis of complex survey data identified relationships between biosecurity, husbandry practices and influenza status. This approach helped to identify gaps on biosecurity and key elements for designing successful strategies to prevent and control swine respiratory diseases in the swine industry.

关键词： Swine biosecurity Husbandry practices Swine farms Multiple correspondence analysis Hierarchical cluster analysis bayesian generalized linear model Epidemiology Swine influenza

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bayesian Conway-Maxwell-Poisson regression models for overdispersed and underdispersed counts

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2021年第13期50卷 3094-3105页

作者： Huang, A. Kim, A. S. I. Univ Queensland Sch Math & Phys St Lucia Qld Australia Univ Technol Sydney Sch Math & Phys Sci Broadway NSW Australia

bayesian models that can handle both overdispersed and underdispersed counts are rare in the literature, perhaps because full probability distributions for dispersed counts are rather difficult to construct. This note takes a first look at bayesian Conway-Maxwell-Poisson regression models that can handle both overdispersion and underdispersion yet retain the parsimony and interpretability of classical count models. The focus is on providing an explicit demonstration of bayesian regression inferences for dispersed counts via a Metropolis-Hastings algorithm. We illustrate the approach on two data analysis examples and demonstrate some favorable frequentist properties via a simulation study.

关键词： bayesian generalized linear model count data overdispersion underdispersion Conway-Maxwell-Poisson

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Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood

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JOURNAL OF HYDROLOGY 2021年 595卷 125663-125663页

作者： Avand, Mohammadtaghi Moradi, Hamidreza Lasboyee, Mehdi Ramazanzadeh Tarbiat Modares Univ Coll Nat Resources & Marine Sci Dept Watershed Management Engn Noor 46414356 Iran Mazandaran Univ Coll Humanities & Social Sci Dept Tourism Management Babolsar Iran

The purpose of this study is to investigate the effects of climate and land use changes on flood susceptibility areas in the Tajan watershed, Iran. To do this, land use changes over the next 20 years (2019-2040) were predicted from land use changes of the past 29 years (1990-2019) using the land change modeler (LCM) method. Future climate change was projected for the next 20 years (2020-2040) based on climate data from 1990 to 2015 using Lars-WG software and two scenarios, RCP2.6 and RCP8.5. Twelve factors that influence flooding and 262 locations of past floods were used to model the spatial pattern of flood susceptibility in the watershed. A random forest (RF) model and a bayesian generalized linear model (GLMbayes) were used to predict areas susceptible to flooding. The results showed that elevation (21.55), distance from river (15.28), land use (11.1), slope (10.58), and rainfall (6.8) are the most important factors affecting flooding in this basin. The factors were modified according to land use changes and climate changes and the models were revised. The land use and climate forecasting in this region indicate that land use change, like decreased forest cover (77.19 km(2)) and reduced rangeland (218.83 km(2)) near rivers and downstream, can be expected and rainfall is projected to increase (under from both scenarios). These changes would result in increased probabilities of flooding in the downstream portion of the watershed and near the sea. The area-under-the-curve evaluation of the models indicates that the RF model more accurately predicted flood probability (0.91) than did the GLMbayes model (0.847).

关键词： Climate change Land use change Random forest bayesian generalized linear model Flood probability Tajan watershed

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bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data

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STRUCTURAL CONTROL & HEALTH MONITORING 2021年第4期28卷 e2699-e2699页

作者： Wang, Y. W. Ni, Y. Q. Zhang, Q. H. Zhang, C. Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Branch Natl Rail Transit Electrificat & Automat Engn Tec Hung HomKowloon & Automat Engn Technol Re Hong Kong Peoples R China

Reliable estimation of wind-induced displacement responses of long-span bridges is critical to evaluating their wind-resistant performance. In this study, two bayesian approaches, bayesian generalized linear model (BGLM) and sparse bayesian learning (SBL), are proposed for characterizing the wind-induced lateral displacement responses of long-span bridges with structural health monitoring (SHM) data. They are fully model-free data-driven approaches, preferable for reckoning the wind-induced total displacement intended for wind-resistant performance assessment. With the measured displacement responses and wind speeds, a BGLM is developed to characterize the nonlinear relationship between the total displacement response and wind speed, where the bayesian model class selection (BMCS) criterion is incorporated to determine the optimal model. In the model formulation by SBL, both wind speed and wind direction are treated as explanatory variables to elicit a probabilistic model with sparse structure. The SBL cleverly makes the resulting model to exempt from overfitting and generalizes well on unseen data. The two formulated models are then utilized to forecast the wind-induced displacement responses in extreme typhoon events beyond the monitoring scope, and the predicted displacement responses are contrasted to the finite element analysis results and the design maximum allowable displacement under the serviceability limit state (SLS). The proposed methods are demonstrated using the monitoring data acquired by GPS sensors and anemometers instrumented on a long-span suspension bridge. The results show that the SBL model is superior to the BGLM for wind-induced displacement response prediction and is amenable to SHM-based evaluation of wind-resistant performance under extreme typhoon conditions.

关键词： bayesian generalized linear model long-span bridge sparse bayesian learning structural health monitoring wind-resistant performance

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Community-based management fails to halt declines of bumphead parrotfish and humphead wrasse in Roviana Lagoon, Solomon Islands

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CORAL REEFS 2019年第3期38卷 455-465页

作者： Hamilton, Richard J. Hughes, Alec Brown, Christopher J. Leve, Tingo Kama, Warren Asia Pacific Resource Ctr Nat Conservancy 48 Montague Rd South Brisbane Qld 4101 Australia James Cook Univ ARC Ctr Excellence Coral Reef Studies Townsville Qld 4811 Australia Wildlife Conservat Soc POB 98 Munda Western Provinc Solomon Islands Griffith Univ Australian Rivers Inst Coast & Estuaries 170 Kessels Rd Nathan Qld 4111 Australia Griffith Univ Sch Environm & Sci 170 Kessels Rd Nathan Qld 4111 Australia

Community-based fisheries management that integrates local knowledge and existing user rights is often seen as a solution to the failures of top-down fisheries management in the Pacific. In Roviana Lagoon, Western Solomon Islands, a network of community-based marine protected areas (MPAs) was established in the early 2000s to conserve declining populations of bumphead parrotfish (Bolbometopon muricatum) and other locally valuable fish species such as humphead wrasse (Cheilinus undulatus). We aimed to evaluate the success of these protected areas at preventing declines of B. muricatum and C. undulatus. We conducted 27 underwater visual census (UVC) surveys at permanent passage and outer reef monitoring sites in Roviana Lagoon in 2018 and compared our findings with results from 72 UVC surveys that we had conducted at the same sites 18yrs earlier. We also interviewed Roviana spearfishers about their maximum nightly B. muricatum catches from 2018, the early 2000s and the 1980s. Abundances of all B. muricatum and C. undulatus sighted on UVC surveys declined by 62% and 57%, respectively, between 2000 and 2018, and abundances of adult B. muricatum and C. undulatus declined by 78% and 72%, respectively, over the same period. Using a joint model of B. muricatum abundance and its reported maximum catch, we estimated that in 2018 the population of B. muricatum was 8% of its 1980's abundance. By modelling projected rates of decline over three generations, we show that populations of B. muricatum and C. undulatus in Roviana Lagoon meet the IUCN Red List thresholds for Critically Endangered (CR). The probable causes of these declines are sustained fishing pressure, poor enforcement of community-based management measures and loss of fish nursery habitats due to logging. Our findings suggest urgent co-management of the ridge-to-reef system is needed to prevent further fish population declines in Roviana Lagoon.

关键词： Bolbometopon muricatum Cheilinus undulatus Monitoring Local knowledge Coral Triangle bayesian generalized linear model IUCN Red List

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Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and bayesian Machine Learning models

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IEEE ACCESS 2020年 8卷 145564-145576页

作者： Mosavi, Amirhosein Hosseini, Farzaneh Sajedi Choubin, Bahram Goodarzi, Massoud Dineva, Adrienn A. Ton Duc Thang Univ Environm Qual Atmospher Sci & Climate Chang Res G Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Univ Tehran Fac Nat Resources Reclamat Arid & Mountainous Reg Dept Karaj *** Iran AREEO West Azarbaijan Agr & Nat Resources Res & Educ Ct Soil Conservat & Watershed Management Res Dept Orumiyeh *** Iran AREEO SCWMRI Tehran *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Obuda Univ Kalman Kando Fac Elect Engn H-1034 Budapest Hungary

Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and bayesian generalized linear model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (>0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

关键词： Salinity (geophysical) Predictive models Numerical models Lakes Irrigation Machine learning Biological system modeling Groundwater salinity hazard recursive feature elimination stochastic gradient boosting rotation forest bayesian generalized linear model

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Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis

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BMC MEDICAL RESEARCH METHODOLOGY 2020年第1期20卷 24-24页

作者： Stuehler, E. Brauner, S. Lionetto, F. Heer, Y. Jules, E. Westermann, C. Bergmann, A. van Hoevell, P. PwC Digital Serv Zurich Switzerland NeuroTransData Neuburg Germany

Background Personalized healthcare promises to successfully advance the treatment of heterogeneous neurological disorders such as relapsing remitting multiple sclerosis by addressing the caveats of traditional healthcare. This study presents a framework for personalized prediction of treatment response based on real-world data from the NeuroTransData network. Methods A framework for personalized prediction of response to various treatments currently available for relapsing remitting multiple sclerosis patients was proposed. Two indicators of therapy effectiveness were used: number of relapses, and confirmed disability progression. The following steps were performed: (1) Data preprocessing and selection of predictors according to quality and inclusion criteria;(2) Implementation of hierarchical bayesian generalized linear models for estimating treatment response;(3) Validation of the resulting predictive models based on several performance measures and routines, together with additional analyses that focus on evaluating the usability in clinical practice, such as comparing predicted treatment response with the empirically observed course of multiple sclerosis for different adherence profiles. Results The results revealed that the predictive models provide robust and accurate predictions and generalize to new patients and clinical sites. Three different out-of-sample validation schemes (10-fold cross-validation, leave-one-site-out cross-validation, and excluding a test set) were employed to assess generalizability based on three different statistical performance measures (mean squared error, Harrell's concordance statistic, and negative log-likelihood). Sensitivity to different choices of the priors, to the characteristics of the underlying patient population, and to the sample size, was assessed. Finally, it was shown that model predictions are clinically meaningful. Conclusions Applying personalized predictive models in relapsing remitting multiple sclerosis patient

关键词： Relapsing remitting multiple sclerosis Personalized medicine Clinical decision support Personalized health record Personalized predictive models bayesian generalized linear model

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