scholarly journals CLIMATE-DRIVEN STATISTICAL MODELS AS EFECTIVE PREDICTIONS OF LOCAL DENGUE INDICENCE IN COSTA RICA: A GENERALIZED ADDITIVE MODEL AND RANDOM FOREST APPROACH

2019 ◽  
Vol 27 (1) ◽  
pp. 1-21
Author(s):  
PAOLA VÁSQUEZ ◽  
ANTONIO LORÍA ◽  
FABIO SÁNCHEZ ◽  
LUIS ALBERTO BARBOZA
Keyword(s):  
Public Health ◽  
Relative Risk ◽  
Costa Rica ◽  
Random Forest ◽  
Prediction Model ◽  
Statistical Models ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Climate Data ◽  
Tropical Country

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.

scholarly journals Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 422
Author(s):  
Jérémy Gelb ◽  
Philippe Apparicio
Keyword(s):  
Statistical Models ◽  
Noise Exposure ◽  
Generalized Additive Model ◽  
Low Cost ◽  
Additive Model ◽  
Noise Pollution ◽  
Ventilation Rate ◽  
Mixed Effects ◽  
Mobile Data ◽  
Mobile Data Collection

Cyclists are particularly exposed to air and noise pollution because of their higher ventilation rate and their proximity to traffic. However, few studies have investigated their multi-exposure and have taken into account its real complexity in building statistical models (nonlinearity, pseudo replication, autocorrelation, etc.). We propose here to model cyclists’ exposure to air and noise pollution simultaneously in Paris (France). Specifically, the purpose of this study is to develop a methodology based on an extensive mobile data collection using low-cost sensors to determine which factors of the urban micro-scale environment contribute to cyclists’ multi-exposure and to what extent. To this end, we developed a conceptual framework to define cyclists’ multi-exposure and applied it to a multivariate generalized additive model with mixed effects and temporal autocorrelation. The results show that it is possible to reduce cyclists’ multi-exposure by adapting the planning and development practices of cycling infrastructure, and that this reduction can be substantial for noise exposure.

scholarly journals Ambient Sulfate Trends and the Influence of Meteorology

2005 ◽  
Vol 44 (11) ◽  
pp. 1745-1760 ◽  
Author(s):  
Stephen F. Mueller
Keyword(s):  
Statistical Models ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Total Sulfur ◽  
Eastern United States ◽  
Sulfur Species ◽  
Sulfate Sulfur ◽  
Average Change ◽  
Meteorological Influences ◽  
Local Deposition

Abstract Data on atmospheric levels of sulfur dioxide (SO2) and sulfate were examined to quantify changes since 1989. Changes in sulfur species were adjusted to account for meteorological variability. Adjustments were made using meteorological variables expressed in terms of their principal components that were used as predictors in statistical models. Several models were tested. A generalized additive model (GAM)—based in part on nonparametric, locally smoothed predictor functions—computed the greatest association between sulfate and the meteorological predictors. Sulfate trends estimated after a GAM-based adjustment for weather-related influences were found to be primarily downward across the eastern United States by as much as 6.7% per year (average of −2.6% per year), but large spatial variability was noted. The most conspicuous characteristic in the trends was over portions of the Appalachian Mountains where very small (average = −1.6% per year) and often insignificant sulfate changes were found. The Appalachian region also experienced a tendency, after removing meteorological influences, for increases in the ratio RS of sulfate sulfur to total sulfur. Before 1991, this ratio averaged 0.33 across all sites. Appalachian increases in RS were equivalent to 0.07 during 1989–2001 (significant for most sites at the 0.05 level), or nearly 2 times the average change at the other sites. This suggests that conditions over the Appalachians became notably more efficient at oxidizing SO2 into sulfate. Alternatively, subtle changes in local deposition patterns occurred, preferentially in and near mountainous monitoring sites, that changed the SO2–sulfate balance.

scholarly journals A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Shrirang A. Kulkarni ◽  
Jodh S. Pannu ◽  
Andriy V. Koval ◽  
Gabriel J. Merrin ◽  
Varadraj P. Gurupur ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Physical Therapy ◽  
Random Forest ◽  
Generalized Additive Model ◽  
Additive Model ◽  
The United States ◽  
Random Forest Regression ◽  
Key Variables ◽  
Machine Learning Models

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.

scholarly journals Potential distribution models of Sechium tacaco (Pittier) C. Jeffrey in Costa Rica

2021 ◽  
Author(s):  
Luis Ángel Barrera-Guzmán ◽  
Juan Porfirio Legaria-Solano ◽  
Jorge Cadena-Iñiguez ◽  
Jaime Sahagún-Castellanos ◽  
Gabriela Ramírez-Ojeda
Keyword(s):  
Costa Rica ◽  
Potential Distribution ◽  
Design Methodology ◽  
Species Distribution Models ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Area Under The Curve ◽  
San Jose ◽  
Distribution Models ◽  
Bioclimatic Variables

Objective: Determine current and potential distribution of S. tacaco in Costa Rica with seven Species Distribution Models (SDM), in order to optimize the management of S. tacaco genetic resources, aimed at identifying patterns of geographic distribution and possible climatic adaptations allowing to have perspectives on their conservation and genetic breeding. Design/Methodology/Approach: 21 points of occurrence together with 19 bioclimatic variables and altitude were used to evaluate seven machine learning models and an assembly of these. Open-source libraries running in Rstudio were used. Results: Distribution models were inferred by the variables bio1, bio2, bio3, bio4, bio12, bio13, bio14, bio18 y bio19. The generalized additive model obtained the highest values ??of area under the curve (0.96) and True skill statistic (0.90), however, the seven models tested and the assembly showed adequate performance (AUC> 0.5 and TSS> 0.4). Bioclimatic variables related to temperature were the ones with the greatest contribution to the models and the main limitations in the distribution of S. tacaco. Study limitations/implications: Possibly a greater number of occurrence points are required to evaluate distribution models. Findings/Conclusions. Areas with high potential distribution suitability for S. tacaco are found in central valleys of Costa Rica, covering regions of the provinces of Alajuela, Cartago, San José and Puntarenas. These areas can be sources of germplasm for future conservation and breeding studies.

scholarly journals Mathematical and Statistical models as tools for the control of mosquito-borne diseases: The experience of Costa Rica

2021 ◽  
Author(s):  
Fabio Sanchez ◽  
Luis A Barboza ◽  
Paola Vásquez ◽  
Yury E García ◽  
Juan G Calvo ◽  
...  
Keyword(s):  
Public Health ◽  
Costa Rica ◽  
Disease Prevention ◽  
Infectious Diseases ◽  
Statistical Models ◽  
Optimal Strategies ◽  
Health Interventions ◽  
Control Methods ◽  
Public Health Interventions ◽  
Statistical Tools

The modeling of infectious diseases provides valuable input in the development of mitigating strategiesand implementation of public health interventions. We highlight results and current research conductedin Costa Rica using mathematical and statistical tools to develop optimal strategies for mosquito controland mosquito-borne disease prevention/control methods in the country.

scholarly journals Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

2014 ◽  
Vol 48 (3) ◽  
pp. 451-458 ◽  
Author(s):  
Juliana Bottoni de Souza ◽  
Valdério Anselmo Reisen ◽  
Jane Méri Santos ◽  
Glaura Conceição Franco
Keyword(s):  
Relative Risk ◽  
Air Pollutants ◽  
Generalized Additive Model ◽  
Hospital Admissions ◽  
Additive Model ◽  
Principal Component ◽  
Component Analysis ◽  
Additive Models ◽  
Southeastern Brazil ◽  
Linear Modeling

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

GAMLSS for high-variability data: an application to liver fibrosis case

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Andrea Marletta ◽  
Mariangela Sciandra
Keyword(s):  
Risk Factors ◽  
Liver Fibrosis ◽  
Statistical Models ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Finite Mixture Model ◽  
High Variability ◽  
Finite Mixture ◽  
Potential Risk Factors ◽  
Variability Data

AbstractThis article aims to provide rigorous and convenient statistical models for dealing with high-variability phenomena. The presence of discrepance in variance represents a substantial issue when it is not possible to reduce variability before analysing the data, leading to the possibility to estimate an inadequate model. In this paper, the application of Generalized Additive Model for Location, Scale and Shape (GAMLSS) and the use of finite mixture model for GAMLSS will be proposed as a solution to the problem of overdispersion. An application to Liver fibrosis data is illustrated in order to identify potential risk factors for patients, which could determine the presence of the disease but also its levels of severity.

scholarly journals Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Afiqah Syamimi Masrani ◽  
Nik Rosmawati Nik Husain ◽  
Kamarul Imran Musa ◽  
Ahmad Syaarani Yasin
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Warning System ◽  
Weather Data ◽  
Maximum Temperature ◽  
Climate Data ◽  
Weather Variables ◽  
Relevant Variable

Introduction. Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method. All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10 ′ N, longitude 102°18 ′ E) was analysed with dengue data. Result. A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion. Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.

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