scholarly journals Ambient air pollutants, meteorological factors and their interactions affect confirmed cases of COVID-19 in 120 Chinese cities

2020 ◽  
Author(s):  
Jianli Zhou ◽  
Linyuan Qin ◽  
Nan Liu
Keyword(s):  
Regression Analysis ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Meteorological Factors ◽  
Negative Binomial Regression ◽  
Ambient Air ◽  
Chinese Cities ◽  
Ambient Air Pollutants ◽  
Binomial Regression ◽  
The Impact

AbstractEmerging evidences have confirmed effects of meteorological factors on novel coronavirus disease 2019 (COVID-19). However, few studies verify the impact of air pollutants on this pandemic. This study aims to explore the association of ambient air pollutants, meteorological factors and their interactions effect confirmed case counts of COVID-19 in 120 Chinese cities. Here, we collected total confirmed cases of COVID-19 by combining with meteorological factors and air pollutants data from 15th January 2020 to 18th March 2020 in 120 Chinese cities. Spearman correlation analysis was employed to estimate the association between two variables; univariate and multivariate negative binomial regression analysis were applied to explore the effect of air pollutants and meteorological parameters on the COVID-19 confirmed cases. Positive associations were found between the confirmed cases of COVID-19 and carbon monoxide (CO), aerodynamic particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), relative humidity (RH) and air pressure (AP). And negative association was found for sulfur dioxide (SO2). In addition, multivariate negative binomial regression analysis suggested that confirmed cases of COVID-19 was positively correlated with ozone (O3) in lag 0 day while it was negatively associated with wind velocity (WV) in lag 14 days, and the pollutants-meteorological factors interactions also associate with COVID-19. In conclusions, air pollutants and meteorological factors and their interactions all associate with COVID-19.

scholarly journals Lag Effects of Ozone, PM2.5, and Meteorological Factors on COVID-19 New Cases at the Disease Epicenter in Queens, New York

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 357
Author(s):  
Atin Adhikari ◽  
Jingjing Yin
Keyword(s):  
New York ◽  
Environmental Factors ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Meteorological Factors ◽  
Absolute Humidity ◽  
Negative Binomial Regression ◽  
Ambient Air ◽  
Reproductive Number ◽  
Lag Effects

The influences of environmental factors on COVID-19 may not be immediate and could be lagged for days to weeks. This study investigated the choice of lag days for calculating cumulative lag effects of ozone, PM2.5, and five meteorological factors (wind speed, temperature, relative humidity, absolute humidity, and cloud percentages) on COVID-19 new cases at the epicenter of Queens County, New York, before the governor’s executive order on wearing of masks in public places (1 March to 11 April 2020). Daily data for selected air pollutants and meteorological factors were collected from the US EPA Air Quality System, weather observation station of the NOAA National Centers for Environmental Information at John F. Kennedy Airport, and World Weather Online. Negative binomial regression models were applied, including the autocorrelations and trend of the time series, as well as the effective reproductive number as confounders. The effects of ozone, PM2.5, and five meteorological factors were significant on COVID-19 new cases with lag9-lag13 days. Incidence rate ratios (IRRs) were consistent for any lag day choice between lag0 and lag14 days and started fluctuating after lag15 days. Considering moving averages >14 days yielded less reliable variables for summarizing the cumulative lag effects of environmental factors on COVID-19 new cases and considering lag days from 9 to 13 would yield significant findings. Future studies should consider this approach of lag day checks concerning the modeling of COVID-19 progression in relation to meteorological factors and ambient air pollutants.

scholarly journals Association between breakfast skipping and metabolic outcomes by sex, age, and work status stratification

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jun Heo ◽  
Won-Jun Choi ◽  
Seunghon Ham ◽  
Seong-Kyu Kang ◽  
Wanhyung Lee
Keyword(s):  
Regression Analysis ◽  
Negative Binomial ◽  
Metabolic Diseases ◽  
Young Male ◽  
Negative Binomial Regression ◽  
Final Analysis ◽  
Work Status ◽  
Breakfast Skipping ◽  
Metabolic Outcomes ◽  
Binomial Regression

Abstract Background The association between breakfast skipping and abnormal metabolic outcomes remains controversial. A comprehensive study with various stratified data is required. Objective The aim of this study was to investigate the relationship between abnormal metabolic outcomes and breakfast skipping by sex, age, and work status stratification. Methods We used data from the Korea National Health and Nutrition Examination Surveys from 2013 to 2018. A total of 21,193 (9022 men and 12,171 women) participants were included in the final analysis. The risk of metabolic outcomes linked to breakfast skipping was estimated using the negative binomial regression analysis by sex, work status, and age stratification. Results A total of 11,952 (56.4%) participants consumed breakfast regularly. The prevalence of abnormal metabolic outcomes was higher among those with irregular breakfast consumption habits. Among young male workers, negative binomial regression analysis showed that irregular breakfast eaters had a higher risk of abnormal metabolic outcomes, after adjusting for covariates (odds ratio, 1.15; 95% confidence interval, 1.03–1.27). Conclusions The risk of abnormal metabolic outcomes was significant in young men in the working population. Further studies are required to understand the association of specific working conditions (working hours or shift work) with breakfast intake status and the risk of metabolic diseases.

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

scholarly journals PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON

2013 ◽  
Vol 2 (2) ◽  
pp. 6
Author(s):  
PUTU SUSAN PRADAWATI ◽  
KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Response Variable ◽  
Poisson Regression Analysis ◽  
Binomial Regression ◽  
The Mean

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

scholarly journals Customer reactions to a webshop’s service quality

Empirica ◽  
2019 ◽  
Vol 47 (4) ◽  
pp. 699-731
Author(s):  
Franz Hackl ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Service Quality ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Product Categories ◽  
Service Characteristics ◽  
Binomial Regression ◽  
Quality Aspects ◽  
Quality Dimensions ◽  
The Impact

Abstract E-commerce has become an integral part of the world’s economy. In this study we investigate the impact of service quality in e-tailing on site visits and consumer demand. Such an analysis is important given the almost Bertrand-like competitive structure. Our analysis is based on a large representative data set obtained from a price comparison site covering essentially the complete Austrian e-tailing market. Customer evaluations for a broad range of 15 different service characteristics are condensed using factor analysis. Negative binomial regression analysis is used to measure the impact of service quality dimensions on referral requests to online shops for different product categories. Our results show that the most important service quality aspects are those related to the ordering process and the firm’s website performance.

Mandated Representation: Exploring the Consequences of Gender Quota Design on Parliamentary Activity

2020 ◽  
Author(s):  
Eva-Maria Euchner ◽  
Elena Frech
Keyword(s):  
Negative Binomial ◽  
Gender Diversity ◽  
Negative Binomial Regression ◽  
Gender Quotas ◽  
Binomial Regression ◽  
Interdisciplinary Framework ◽  
Research Gap ◽  
And Gender ◽  
Gender Quota ◽  
The Impact

Abstract Although the scholarship on legislative behaviour widely agrees that electoral rules determine parliamentary activities, surprisingly little is known on the impact of gender quotas. We contribute to this research gap by developing an innovative interdisciplinary framework and by exploring it based on a unique dataset on varying gender quota designs throughout EU countries and parties running for the 7th term of the European Parliament (2009–2014). Based on the scholarship on gender diversity in management teams and the research on gendered processes in political parties, we argue that especially mandated gender quotas stimulate processes of social categorisation, intergroup biasing and competition due to a normative mis-fit between conceptions of gender equality and gender quotas, which in turn influences coordination and communication and hence, parliamentary activity more generally. Combining negative-binomial regression models and expert interviews, we indeed find that mandated gender quotas promote ‘individual’ parliamentary activities (e.g. speeches) and tend to impede ‘collaborative’ parliamentary activities (e.g. reports).

scholarly journals Does Car-Sharing Reduce Car-Use? An Impact Evaluation of Car-Sharing in Flanders, Belgium

2020 ◽  
Vol 12 (19) ◽  
pp. 8155
Author(s):  
Donald A. Chapman ◽  
Johan Eyckmans ◽  
Karel Van Acker
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Experimental Methods ◽  
Car Ownership ◽  
Car Sharing ◽  
Car Use ◽  
Binomial Regression ◽  
Quasi Experimental ◽  
Private Car ◽  
The Impact

Private car-use is a major contributor of greenhouse gases. Car-sharing is often hypothesised as a potential solution to reduce car-ownership, which can lead to car-sharing users reducing their car-use. However, there is a risk that car-sharing may also increase car-use amongst some users. Existing studies on the impacts of car-sharing on car-use are often based on estimates of the users’ own judgement of the effects; few studies make use of quasi-experimental methods. In this paper, the impact of car-sharing on car-ownership and car-use in Flanders, Belgium is estimated using survey data from both sharers and non-sharers. The impact on car-use is estimated using zero-inflated negative binomial regression, applied to matched samples of car-sharing users and non-users. The results show that the car-sharing may reduce car-use, but only if a significant number of users reduce their car-ownership. Policy intervention may therefore be required to ensure car-sharing leads to a reduction in car-use by, for example, discouraging car-ownership. Further research using quasi-experimental methods is required to illuminate whether the promise of car-sharing is reflected in reality.

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