scholarly journals Correlational study of air pollution-related diseases (asthma, conjunctivitis, URTI and dengue) in Johor Bahru, Malaysia

2017 ◽  
Vol 13 (4-1) ◽  
pp. 354-361 ◽  
Author(s):  
Aaishah Radziah Jamaludin ◽  
Fadhilah Yusof ◽  
Rahmah Mohd Lokoman ◽  
Zainura Zainoon Noor ◽  
Noreliza Alias ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Sulphur Dioxide ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Influential Factors ◽  
Motor Vehicles ◽  
Binomial Model ◽  
Climate Variables ◽  
Mobile Sources

Four pollution related diseases, namely asthma, conjunctivitis, URTI and dengue will be studied in terms of their trend, behaviour and association with influential factors such as air pollution and climate variables. Two methods were chosen; Poisson Generalized Linear Model and Negative Binomial Model. These methods were used to determine the association between the diseases and their influential factors. This study shows that Sulphur Dioxide (SO2) is the most abundant source that contributes to the diseases. Therefore, the local authorities such as the Department of Environment need to reinforce the law in planning and monitoring the SO2 sources which are produced from fuel combustion in mobile sources and motor vehicles. 

Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

2015 ◽  
Vol 26 (3) ◽  
pp. 545-555 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
John L. Innes ◽  
Xiangqing Ma ◽  
Long Sun ◽  
...  
Keyword(s):  
Linear Model ◽  
Forest Fire ◽  
Generalized Linear Model ◽  
Northeast China ◽  
Climate Variables ◽  
Area Burned

Mixed Generalized Linear Model for Estimating Household Trip Production

2000 ◽  
Vol 1718 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Chang-Jen Lan ◽  
Patricia S. Hu
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Empirical Distribution ◽  
Distribution Functions ◽  
Model Specification ◽  
Parameter Estimates ◽  
Modeling Framework ◽  
Personal Transportation ◽  
Trip Production

An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal Transportation Survey data is presented. A generalized linear model with a mixture of negative binomial probability distribution functions was developed on the basis of characteristics observed from the empirical distribution of household daily trips. This model provides a more flexible framework and a better model specification for analyzing household-specific trip production behavior. Compared with traditional least squares-based regression models, the parameter estimates from the proposed model are more efficient. Although the mean accuracies from the two modeling approaches are comparable, the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric prediction bounds derived from more correct model specification.

scholarly journals Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution

2020 ◽  
Vol 17 (13) ◽  
pp. 4828
Author(s):  
Mei Yang ◽  
Hong Fan ◽  
Kang Zhao
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatiotemporal Analysis ◽  
Yangtze River Delta ◽  
Influential Factors ◽  
Motor Vehicles ◽  
Emission Scenarios ◽  
Fine Grained ◽  
The Impact ◽  
Pm2.5 Concentration

Aiming at improving the air quality and protecting public health, policies such as restricting factories, motor vehicles, and fireworks have been widely implemented. However, fine-grained spatiotemporal analysis of these policies’ effectiveness is lacking. This paper collected the hourly meteorological and PM2.5 data for three typical emission scenarios in Hubei, Beijing–Tianjin–Hebei (BTH), and Yangtze River Delta (YRD). Then, this study simulated the PM2.5 concentration under the same meteorological conditions and different emission scenarios based on a reliable hourly spatiotemporal random forest model ( R 2 exceeded 0.84). Finally, we investigated the fine-grained spatiotemporal impact of restricting factories, vehicles, and fireworks on PM2.5 concentrations from the perspective of hours, days, regions, and land uses, excluding meteorological interference. On average, restricting factories and vehicles reduced the PM2.5 concentration at 02:00, 08:00, 14:00, and 20:00 by 18.57, 16.22, 25.00, and 19.07 μ g / m 3 , respectively. Spatially, it had the highest and quickest impact on Hubei, with a 27.05 μ g / m 3 decrease of PM2.5 concentration and 17 day lag to begin to show significant decline. This was followed by YRD, which experienced a 23.52 μ g / m 3 decrease on average and a 23 day lag. BTH was the least susceptible; the PM2.5 concentration decreased by only 8.2 μ g / m 3 . In addition, influenced by intensive human activities, the cultivated, urban, and rural lands experienced a larger decrease in PM2.5 concentration. These empirical results revealed that restricting factories, vehicles, and fireworks is effective in alleviating air pollution and the effect showed significant spatiotemporal heterogeneity. The policymakers should further investigate influential factors of hourly PM2.5 concentrations, combining with local geographical and social environment, and implement more effective and targeted policies to improve local air quality, especially for BTH and the air quality at morning and night.

Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

2020 ◽  
Vol 191 ◽  
pp. 110106 ◽  
Author(s):  
Jônatas T. Belotti ◽  
Diego S. Castanho ◽  
Lilian N. Araujo ◽  
Lucas V. da Silva ◽  
Thiago Antonini Alves ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Air Pollution Epidemiology ◽  
Model Approach

scholarly journals Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

2021 ◽  
Author(s):  
Ratih Oktri Nanda ◽  
Aldilas Achmad Nursetyo ◽  
Aditya Lia Ramadona ◽  
Muhammad Ali Imron ◽  
Anis Fuad ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Human Mobility ◽  
Management Strategies ◽  
Principal Component ◽  
Outbreak Management ◽  
Community Mobility

Background Human mobility could act as a vector to facilitate the spread of infectious diseases. In response to the COVID-19 pandemic, Google Community Mobility Reports (CMR) provide the necessary data to explore community mobility further. Therefore, we aimed to examine the relationship between community mobility on COVID-19 dynamics in Jakarta, Indonesia. Methods We utilized the mobility data from Google from February 15 to December 31, 2020. We explored several statistical models to estimate the COVID-19 dynamics in Jakarta. Model 1 was a Poisson Regression Generalized Linear Model (GLM), Model 2 was a Negative Binomial Regression Generalized Linear Model (GLM), and Model 3 was a Multiple Linear Regression (MLR). Results We found that Multiple Linear Regression (MLR) with some adjustments using Principal Component Analysis (PCA) was the best fit model. It explained 52% of COVID-19 cases in Jakarta (R-Square: 0.52, p<0.05). All mobility variables were significant predictors of COVID-19 cases (p<0.05). More precisely, about 1% change in grocery and pharmacy would contribute to a 4.12% increase of the COVID-19 cases in Jakarta. Retails and recreations, workplaces, transit stations, and parks would result in 3.11%, 2.56%, 2.26%, and 1.93% of more COVID-19 cases, respectively. Conclusion Our study indicates that increased mobility contributes to increased COVID-19 cases. This finding will be beneficial to assist policymakers to have better outbreak management strategies, to anticipate increased COVID-19 cases in the future at certain public places and during seasonal events such as annual religious holidays or other long holidays in particular.

scholarly journals Pemodelan Jumlah Kasus Malaria di Indonesia Menggunakan Generalized Linear Model

2021 ◽  
Vol 5 (1) ◽  
pp. 112-120
Author(s):  
Vera Maya Santi ◽  
Abi Wiyono ◽  
Sudarwanto
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Likelihood Estimation ◽  
Binomial Model

Generalized Linear Model (GLM) telah banyak digunakan untuk memodelkan berbagai macam tipe data dimana distribusi dari variabel respon merupakan distribusi yang termasuk dalam distribusi keluarga eksponensial. Contoh umum dari distribusi keluarga eksponensial adalah distribusi Poisson dan Binomial. Model regresi GLM mendeskripsikan struktur dari variabel prediktor, sedangkan fungsi penghubung secara khusus mendeskripsikan hubungan antara model regresi dengan nilai ekspektasi dari variabel respon. Tujuan dari artikel ini adalah mendapatkan variabel-variabel prediktor yang berpengaruh signifikan terhadap model. Metode Maximum Likelihood Estimation digunakan untuk mencari estimasi dari nilai parameter regresi model. Jumlah kasus malaria di Indonesia diidentifikasi berdistribusi Poisson. Terdapat 3 variabel prediktor yang berpengaruh signifikan terhadap jumlah kasus malaria di Indonesia, yaitu persentase rumah tangga yang memiliki akses sanitasi layak, jumlah kabupaten/kota yang menyelenggarakan tatanan kawasan kesehatan dan jumlah kabupaten/kota yang melakukan pengendalian vektor terpadu.

scholarly journals Non-Convex Penalized Estimation of Count Data Responses via Generalized Linear Model (GLM)

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Rasaki Olawale Olanrewaju ◽  
Johnson Funminiyi Ojo
Keyword(s):  
Sample Size ◽  
Linear Model ◽  
Count Data ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Estimation Procedure ◽  
Absolute Deviation ◽  
Penalized Estimation ◽  
Binomial Regression ◽  
Minimax Concave Penalty

This study provided a non-convex penalized estimation procedure via Smoothed Clipped Absolute Deviation (SCAD) and Minimax Concave Penalty (MCP) for count data responses to checkmate the problem of covariates exceeding the sample size . The Generalized Linear Model (GLM) approach was adopted in obtaining the penalized functions needed by the MCP and SCAD non-convex penalizations of Binomial, Poisson and Negative-Binomial related count responses regression. A case study of the colorectal cancer with six (6) covariates against sample size of five (5) was subjected to the non-convex penalized estimation of the three distributions. It was revealed that the non-convex penalization of Binomial regression via MCP and SCAD best explained four un-penalized covariates needed in determining whether surgical or therapy ideal for treating the turmoil.

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