scholarly journals The burden associated with ambient PM2.5 and meteorological factors in Guangzhou, China, 2012–2016: A generalized additive modeling of temporal years of life lost

Chemosphere ◽  
2018 ◽  
Vol 212 ◽  
pp. 705-714 ◽  
Author(s):  
Xiao Lin ◽  
Yu Liao ◽  
Yuantao Hao
Keyword(s):  
Meteorological Factors ◽  
Years Of Life Lost ◽  
Generalized Additive Modeling ◽  
Additive Modeling

scholarly journals Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis

2020 ◽  
Author(s):  
Zonglin He ◽  
Yiqiao Chin ◽  
Jian Huang ◽  
Yi He ◽  
Babatunde O. Akinwunmi ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Disease Transmission ◽  
Large Scale ◽  
Meteorological Factors ◽  
Pearson Correlation ◽  
Average Temperature ◽  
Generalized Additive Modeling ◽  
Geographical Regions ◽  
Asian Cities ◽  
Additive Modeling

AbstractAIMTo investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities.METHODPearson’s correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.RESULTSThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<0.01), Shanghai (r=-0.471, P<0.01), and Guangzhou (r=-0.530, P<0.01), yet in contrast, positively correlated with that in Japan (r=0.441, P<0.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Nevertheless, the results were inconsistent across cities and lagged time, suggesting meteorological factors were unlikely to greatly influence the COVID-19 epidemic.CONCLUSIONThe associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and lagged time. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.Significance statementWith increasing COVID-19 cases across China and the world, and previous studies showing that meteorological factors may be associated with infectious disease transmission, the saying has it that when summer comes, the epidemic of COVID-19 may simultaneously fade away. We demonstrated the influence of meteorological factors on the daily domestic new cases of coronavirus disease (COVID-19) in nine Asian cities. And we found that the associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and time. We think this important topic may give better clues on prevention, management, and preparation for new events or new changes that could happen in the COVID-19 epidemiology in various geographical regions and as we move towards Summer.

Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

2021 ◽  
pp. 1-8
Author(s):  
Koji Miwa ◽  
Harald Baayen
Keyword(s):  
Lexical Decision ◽  
Tensor Product ◽  
Nonlinear Interaction ◽  
Mixed Model ◽  
Nonlinear Effects ◽  
Regression Splines ◽  
Generalized Additive Mixed Model ◽  
Generalized Additive Modeling ◽  
Additive Modeling ◽  
Cross Language

Abstract This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

scholarly journals Combining probability scores to optimize clinical use of the NICHD Neonatal BPD Outcome Estimator

2021 ◽  
Vol 16 (9) ◽  
pp. 3-13
Author(s):  
Rebekah Leigh ◽  
John B. Tan ◽  
Shirin DeGiorgio ◽  
Minha Cha ◽  
Chelsea Kent ◽  
...  
Keyword(s):  
Outcome Prediction ◽  
Population Distribution ◽  
Clinical Use ◽  
Grid Search ◽  
Optimal Cutoff ◽  
Original Population ◽  
Very Preterm Infants ◽  
Very Preterm ◽  
Generalized Additive Modeling ◽  
Additive Modeling

Objective: Bronchopulmonary dysplasia (BPD) continues to prevail among very preterm infants. While NICHD BPD Outcome Estimator is easy to use, the clinical interpretation remains challenging. This study aims to optimize its use. Study Design: A retrospective study was conducted with 469 infants born between 2015 and 2020. Data were entered into the Estimator to obtain probability scores. Trajectories of the probability scores were modeled using generalized additive modeling. The optimal cutoff number for predicting severe BPD or death was identified by a grid search from a range established by the original population distribution and the ROC curve. Result: Combining probability scores from the severe and death categories and the no-BPD and mild categories may improve BPD outcome prediction. A cutoff of 21% combining outcome probabilities from severe and death categories is predictive of severe BPD or death. Conclusion: Combining probability scores of different categories improves BPD outcome prediction.

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