Two-part predictive modeling for COVID-19 cases and deaths in the U.S.

COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 observations and 18 variables, which is compiled from 5 data sources from Kaggle. A two-part modeling framework, in which the first part is a logistic classifier and the second part includes machine learning or statistical smoothing methods, is introduced to model the highly skewed distribution of COVID-19 cases and deaths. We also aim to understand what factors are most relevant to COVID-19's occurrence and fatality. Evaluation criteria such as root mean squared error (RMSE) and mean absolute error (MAE) are used. We find that the two-part XGBoost model perform best with predicting the entire distribution of COVID-19 cases and deaths. The most important factors relevant to either COVID-19 cases or deaths include population and the rate of primary care physicians.

Urinary glyphosate and AMPA levels in a cross-sectional study of postmenopausal women: Associations with organic eating behavior and dietary intake.

Animal and epidemiologic studies suggest that there may be adverse health effects from exposure to glyphosate, the most highly used pesticide in the world, and its metabolite aminomethylphosphonic acid (AMPA). Meanwhile, consumption of organic foods (presumably grown free of chemical pesticides) has increased in recent years. However, there have been limited biomonitoring studies assessing the levels of human glyphosate and AMPA exposure in the United States. We examined urinary levels of glyphosate and AMPA in the context of organic eating behavior in a cohort of healthy postmenopausal women residing in Southern California and evaluated associations with demographics, dietary intake, and other lifestyle factors. 338 women provided two first-morning urine samples and at least one paired 24-h dietary recall reporting the previous day's dietary intake. Urinary glyphosate and AMPA were measured using LC-MS/MS. Participants reported on demographic and lifestyle factors via questionnaires. Potential associations were examined between these factors and urinary glyphosate and AMPA concentrations. Glyphosate was detected in 89.9% of urine samples and AMPA in 67.2%. 37.9% of study participants reported often or always eating organic food, 30.2% sometimes, and 32.0% seldom or never. Frequency of organic food consumption was associated with several demographic and lifestyle factors. Frequent organic eaters had significantly lower urinary glyphosate and AMPA levels, but not after adjustment for covariates. Grain consumption was significantly associated with higher urinary glyphosate levels, even among women who reported often or always eating organic grains. Soy protein and alcohol consumption as well as high frequency of eating fast food were associated with higher urinary AMPA levels. In conclusion, in the largest study to date examining paired dietary recall data and measurements of first-void urinary glyphosate and AMPA, the vast majority of subjects sampled had detectable levels, and significant dietary sources in the American diet were identified.

Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Aims: The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U.S. adults using a cross-sectional data from participants in a wellness improvement program. Methods: Logistic regression was used to find the relationship between individual risk factors, predictor and cardiometabolic risk. Supervised machine learning methods were used to predict risk and produce a ranking of variables' importance. A clustering method was used to identify subpopulations of interest. Predictors were divided into those that are nonmodifiable and those that are modifiable. Results: The population comprised 217,254 adults of whom 8.1% had diabetes. Using logistic regression, six variables were identified to be negatively related and eleven were positively related to cardiometabolic risk. Three supervised machine learning classifiers (random forest, gradient boosting, and bagging) were applied with average AUC to be 0.806. Each classifier also produced a ranking of variables' importance. Four subgroups were identified with a k-medoid clustering algorithm, which were mainly distinguished by gender and diabetes status. Conclusions: The study illustrates that machine learning is an important addition to traditional logistic regression in terms of identifying important cardiometabolic risk factors and ranking their importance and the potential for interventions based on lifestyle and medications at an individual level.

Shape selection in Landsat time series: a tool for monitoring forest dynamics.

We present a new methodology for fitting nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral band or index of choice in temporal Landsat data, our method delivers a smoothed rendition of the trajectory constrained to behave in an ecologically sensible manner, reflecting one of seven possible 'shapes'. It also provides parameters summarizing the patterns of each change including year of onset, duration, magnitude, and pre- and postchange rates of growth or recovery. Through a case study featuring fire, harvest, and bark beetle outbreak, we illustrate how resultant fitted values and parameters can be fed into empirical models to map disturbance causal agent and tree canopy cover changes coincident with disturbance events through time. We provide our code in the r package ShapeSelectForest on the Comprehensive R Archival Network and describe our computational approaches for running the method over large geographic areas. We also discuss how this methodology is currently being used for forest disturbance and attribute mapping across the conterminous United States.