Determinants of COVID-19 mortality in the United States dataset(BrainX)

With the current COVID-19 pandemic, there have been various principal questions left unanswered. In response to these vital questions, multiple leading health professionals and researchers have brought forward a new data set. <br>This data set uses several trusted sources to provide reliable information relating to the socioeconomic, racial, weather, healthcare resource utilization and travel data from all the 50 states of the United States of America including District of Columbia in one dataset. The dataset includes numerous possible determinants of COVID-19 spread and mortality, all organized in a simple spreadsheet. With data from all fifty states covering over four general categories and twenty-two specific contributing factors, everything is available in one place. COVID-19 positive rates and mortality in the dataset were obtained from https://covidtracking.com/data. All the data is accurate as of April 30,2020 as reported through the sources.<br>Two researchers collected data from available resources from the reliable governmental and non-governmental sources.(See article and source table references below).<br>With this dataset, explainable machine learning models showing relationship of these determinants with COVID-19 mortality in the United States cases were created. Ref: Mathur P, Sethi T, Mathur A, et al. Explainable machine learning models to understand determinants of COVID-19 mortality in the United States. <i>medRxiv. </i>2020:2020.2005.2023.20110189. Source table for the dataset is available as supplemental to this article.<br>This particular dataset was created for the purpose of continuing research into COVID-19. However, there are many other uses for this large dataset. With information from all 50 states and the District of Columbia, many US statistics can be compared to other statistics or countries. The data from this dataset can also be used to make new datasets with different purposes. <br>

在当前新型冠状病毒肺炎（COVID-19）大流行背景下，诸多核心问题尚未得到解答。为回应这些关键议题，多位顶尖卫生专业人士与研究者推出了全新数据集。<br>本数据集依托多个权威数据源，整合了美国全境50个州及哥伦比亚特区的多维度可靠信息，涵盖社会经济、种族、气象、医疗资源利用及出行相关数据。数据集收录了新冠病毒肺炎传播与死亡的诸多潜在影响因素，全部整理为简易电子表格格式。该数据集覆盖全美50州的数据，涉及4大类共22项具体影响因子，所有信息均可一站式获取。本数据集内的新冠病毒肺炎阳性率及病死率数据取自https://covidtracking.com/data，所有数据截至2020年4月30日，均按原始数据源公示内容校准无误。<br>两位研究者从可靠的政府及非政府公开资源中采集了本数据集所需的数据（详见下文的文献及数据源表格引用说明）。<br>依托本数据集，研究者已构建可解释机器学习模型，用以阐释上述影响因子与美国境内新冠病毒肺炎病例病死率的关联关系。参考文献：Mathur P, Sethi T, Mathur A, 等. 阐释美国境内新冠病毒肺炎病死率影响因子的可解释机器学习模型. medRxiv, 2020:2020.2005.2023.20110189. 本数据集的数据源表格可作为本文的补充材料获取。<br>本数据集最初专为持续开展新冠病毒肺炎相关研究而构建，但亦可应用于诸多其他场景。依托全美50州及哥伦比亚特区的相关数据，研究者可对比美国境内多项统计指标与其他国家或地区的同类指标。此外，本数据集还可用于衍生构建具备不同应用目标的新型数据集。