scholarly journals Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 377 ◽  
Author(s):  
Zhangwen Su ◽  
Haiqing Hu ◽  
Mulualem Tigabu ◽  
Guangyu Wang ◽  
Aicong Zeng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Stable Model ◽  
Wildfire Occurrence ◽  
Great Xing’An Mountains ◽  
Binomial Regression ◽  
The Impact ◽  
The Relationship ◽  
Better Than

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P < 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P < 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

Managing Threats on the High Seas: The Role of Naval Bases on Reducing Maritime Piracy

2021 ◽  
pp. 0095327X2110494
Author(s):  
Orlandrew E. Danzell ◽  
Jacob A. Mauslein ◽  
John D. Avelar
Keyword(s):  
Global Economy ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Security Issue ◽  
Maritime Piracy ◽  
Time Series Dataset ◽  
Binomial Regression ◽  
The Impact

Weak coastal states often lack an adequate, sustained naval presence to monitor and police their territorial waters. Unpatrolled waters, both territorial and otherwise, may provide pirates with substantial financial opportunities that go far beyond any single country. Maritime piracy costs the global economy on average USD 24 billion per year. This research explores the impact of naval bases on acts of piracy to determine if naval presence can decrease the likelihood of piracy. To examine this important economic and national security issue, our research employs a zero-inflated negative binomial regression model. We also rely upon a newly constructed time-series dataset for the years 1992–2018. Our study shows that the presence of naval bases is essential in helping maritime forces combat piracy. Policymakers searching for options to combat piracy should find the results of this study especially useful in creating prescriptive approaches that aid in solving offshore problems.

scholarly journals Connecting Users to Articles: An Analysis of the Impact of Article Level Linking on Journal Use Statistics

2019 ◽  
Vol 14 (4) ◽  
pp. 38-51
Author(s):  
Michelle Swab
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Sciences ◽  
Journal Ranking ◽  
Journal Title ◽  
Negative Binomial Regression Model ◽  
Electronic Journal ◽  
Electronic Resource ◽  
Binomial Regression ◽  
The Impact

Abstract Objective – Electronic resource management challenges and “big deal” cancellations at one Canadian university library contributed to a situation where a number of electronic journal subscriptions at the university’s health sciences library lacked article level linking. The aim of this study was to compare the usage of journals with article level linking enabled to journals where only journal level linking was available or enabled. Methods – A list of electronic journal title subscriptions was generated from vendor and subscription agent invoices. Journal titles were eligible for inclusion if the subscription was available throughout 2018 on the publisher’s platform, if the subscription costs were fully funded by the health sciences library, and if management of the subscription required title-by-title intervention by library staff. Of the 356 journal titles considered, 302 were included in the study. Negative binomial regression was performed to determine the effect of journal vs. article level linking on total COUNTER Journal Report 1 (JR1) successful full-text article requests for 2018, controlling for journal publisher, subject area, journal ranking, and alternate aggregator access. Results – The negative binomial regression model demonstrated that article level linking had a significant, positive effect on total 2018 JR1 (coef: 0.645; p < 0.001). Article level linking increased the expected total JR1 by 90.7% when compared to journals where article level linking was not available or enabled. Differences in predicted usage between journals with article level linking and those without article level linking remained significant at various journal ranking levels. This suggests that usage of both smaller, more specialized journals (e.g., Journal of Vascular Research) and larger, general journals (e.g., New England Journal of Medicine) increases when article level linking is enabled. Conclusions – This study provides statistical evidence that enabling article level linking has a positive impact on journal usage at one academic health sciences library. Although further study is needed, academic libraries should consider enabling article level linking wherever possible in order to facilitate user access, maximize the value of journal subscriptions, and improve convenience for users.

Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

2018 ◽  
Vol 9 (2) ◽  
pp. 333-341 ◽  
Author(s):  
Leda G. Ardiles ◽  
Yara S. Tadano ◽  
Silvano Costa ◽  
Viviana Urbina ◽  
Maurício N. Capucim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Relationship

scholarly journals Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces

2020 ◽  
Author(s):  
Jishan Ahmed ◽  
Md. Hasnat Jaman ◽  
Goutam Saha ◽  
Pratyya Ghosh
Keyword(s):  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Environmental Data ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Social ◽  
The Impact ◽  
Best Fit ◽  
Vital Factor

The main goal of this article is to demonstrate the impact of environmental data on the spreading of Covid-19. In this research, data has been collected from 70 cities/provinces that are affected by Covid-19. Here, environmental data refers to temperatures, humidity and population density in each of these cities/provinces. This data has been analyzed using statistical models such as Poisson, Quasi-Poisson and negative Binomial. It is found that a negative Binomial regression model is the best fit for our data. Our results reveal that average high temperature is the vital factor to slow down the spread of Covid-19. In addition, higher population density found to be an important factor for the quick spreading of Covid-19 where it is quite impossible to maintain the social distance and the virus can spread easily.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

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