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The lockdown policies enacted in the spring of 2020, in response to the growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, have remained a contentious policy tool due to the variability of outcomes they produced for some populations. While ongoing research has illustrated the unequal impact of Coronavirus disease (COVID-19) on minority populations, research in this area has been unable to fully explain the mechanisms that produce these findings. To understand why some groups have been at greater risk of contracting COVID-19, we employ structural inequality theory to better understand how inequality may impact disease transmission in a pandemic. We used a novel approach that enabled us to focus on the microprocesses of structural inequality at the zip code level to study the impact of stay-at-home pandemic policies on COVID-19 positive case rates in an urban setting across three periods of policy implementation. We then analyzed data on traffic volume, income, race, occupation, and instances of COVID-19 positive cases for each zip code in Salt Lake County, Utah (USA) between 17 February 2020 and 12 June 2020. We found that higher income, percent white, and white-collar zip codes had a greater response to the local stay-at-home order and reduced vehicular traffic by nearly 50% during lockdown. The least affluent zip codes only showed a 15% traffic decrease and had COVID-19 rates nearly 10 times higher. At this level of granularity, income and occupation were both associated with COVID-19 outcomes across all three stages of policy implementation, while race was only predictive of outcomes after the lockdown period. Our findings illuminate underlying mechanisms of structural inequality that may have facilitated unequal COVID-19 incidence rates. This study illustrates the need for more granular analyses in policy research and adds to the literature on how structural factors such as income, race, and occupation contribute to disease transmission in a pandemic.
Daniel Mendoza; Tabitha Benney; Rajive Ganguli; Rambabu Pothina; Cheryl Pirozzi; Cameron Quackenbush; Samuel Baty; Erik Crosman; Yue Zhang. The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas. COVID 2021, 1, 186 -202.
AMA StyleDaniel Mendoza, Tabitha Benney, Rajive Ganguli, Rambabu Pothina, Cheryl Pirozzi, Cameron Quackenbush, Samuel Baty, Erik Crosman, Yue Zhang. The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas. COVID. 2021; 1 (1):186-202.
Chicago/Turabian StyleDaniel Mendoza; Tabitha Benney; Rajive Ganguli; Rambabu Pothina; Cheryl Pirozzi; Cameron Quackenbush; Samuel Baty; Erik Crosman; Yue Zhang. 2021. "The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas." COVID 1, no. 1: 186-202.
To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.
Rajive Ganguli; Preston Miller; Rambabu Pothina. Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. Minerals 2021, 11, 776 .
AMA StyleRajive Ganguli, Preston Miller, Rambabu Pothina. Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. Minerals. 2021; 11 (7):776.
Chicago/Turabian StyleRajive Ganguli; Preston Miller; Rambabu Pothina. 2021. "Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine." Minerals 11, no. 7: 776.