Publications by authors named "James Sharpnack"
Article Synopsis
- Wastewater-based epidemiology (WBE) helps track COVID-19 trends and can complement clinical testing, but the relationship between wastewater and clinical data at smaller community levels is not well understood.
- The study, conducted in Davis, California, tests a new method combining the expectation maximization (EM) algorithm with Markov Chain Monte Carlo (MCMC) to better estimate missing data in wastewater testing compared to traditional nondetect methods.
- Results show that the new method may lead to better correlation between community-scale wastewater and clinical data, and the research proposes a way to integrate clinical and wastewater data on a more localized scale for improved public health monitoring.
Article Synopsis
- The study aims to improve the early diagnosis of postpartum diseases in dairy cows by analyzing high-frequency sensor data and comparing the performance of different machine learning algorithms.
- Researchers evaluated the impact of various time windows and decision thresholds on classifier accuracy while considering individual cow factors and farm activities.
- The results show that the Random Forest algorithm outperformed the others (k-NN and SVM) in identifying behavioral patterns linked to metritis, with distinct daily behavior changes observed in the cows.
Article Synopsis
- As of May 22, 2021, California reported nearly 3.7 million COVID-19 infections and about 61,722 related deaths, emphasizing the importance of non-pharmaceutical interventions and vaccine distribution for controlling the virus.
- * Projections from a real-time Bayesian model suggest that if restrictions ease without significant vaccine uptake, California could see a sharp increase in cases and deaths after reopening on June 15, 2021.
- * Enhancing vaccine coverage by 30% could significantly reduce projected cases and deaths by 26.1% and 17.9%, respectively, highlighting the need for continued social distancing and public health measures, especially in communities with lower vaccination rates.*
Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?
Proc Natl Acad Sci U S A
December 2021
Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective.
View Article and Find Full Text PDFThe COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends.
View Article and Find Full Text PDFArticle Synopsis
- * Findings reveal that Veterans' self-reported symptoms differ significantly based on whether they served in the Vietnam, Desert Storm, or Post-9/11 eras, even after accounting for gender and combat exposure.
- * Implications suggest that these era-related differences should be considered in clinical settings and research involving the PAI and similar personality assessments in VA healthcare and trauma treatment.
The fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the [Formula: see text]-nearest-neighbours fused lasso, involves computing the [Formula: see text]-nearest-neighbours graph of the design points and then performing the fused lasso over this graph.
View Article and Find Full Text PDFIntroduction: Despite apparently complete surgical resection, approximately half of resected early-stage lung cancer patients relapse and die of their disease. Adjuvant chemotherapy reduces this risk by only 5% to 8%. Thus, there is a need for better identifying who benefits from adjuvant therapy, the drivers of relapse, and novel targets in this setting.
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