Mark J. Panaggio

Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA

Diba Khan, Mark J. Panaggio, Molly Gallagher, P. B. Graff et autres

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Enhancing the Management of Deployment-Limiting Medical Conditions in the U.S. Department of Navy Leveraging Electronic Health Records and Predictive Analytics Methods

Çağlar Çağlayan, Mark J. Panaggio, Raghav Ramachandran, Winnie Wang et autres

INTRODUCTION: Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be …

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Correction: Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model

Mark J. Panaggio, Mike Fang, Hyunseung Bang, Paige A. Armstrong et autres

[This corrects the article DOI: 10.1371/journal.pone.0292354.].

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Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model

Mark J. Panaggio, Mike Fang, Hyunseung Bang, Paige A. Armstrong et autres

During the COVID-19 pandemic, many public schools across the United States shifted from fully in-person learning to alternative learning modalities such as hybrid and fully remote learning. In this study, data from 14,688 unique school districts from August 2020 to June 2021 …

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On the Mark: Modeling and Forecasting for Public Health Impact

Mark J. Panaggio, Shelby Wilson, Jeremy Ratcliff, Luke C. Mullany et autres

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The Discrete Brachistochrone

David J. Gaebler, Mark J. Panaggio, Timothy J. Pennings

SummaryA discrete brachistochrone is the fastest piecewise linear ramp between fixed endpoints with a given number of segments. This article introduces a new conceptual framework for discrete brachistochrones, proves their two fundamental symmetry properties, and examines the manner in which they converge …

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Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators ― United States, October 1, 2020–March 22, 2023

Heather M. Scobie, Mark J. Panaggio, Alison M. Binder, Molly Gallagher et autres

lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of …

Correlations and Timeliness of National Covid-19 Surveillance Data Sources and Indicators

P. B. Graff, Heather M. Scobie, Mark J. Panaggio, Alison M. Binder et autres

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The Areas of Concern Continuum and Predicting Emerging Hotspots During the Covid-19 Pandemic

Philip Graff, Vijay Narayan, Mark J. Panaggio, Catherine Schwartz

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Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model

Mark J. Panaggio, Mike Fang, Hyunseung Bang, Paige A. Armstrong et autres

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September …

The United States COVID-19 Forecast Hub dataset

Estee Y. Cramer, Yuxin Huang, Yijin Wang, Evan L Ray et autres

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University …

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Gecko: A time-series model for COVID-19 hospital admission forecasting

Mark J. Panaggio, Kaitlin Rainwater‐Lovett, Paul J. Nicholas, Mike Fang et autres

During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health …

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