Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

COVID-19 Progression: A County-Level Analysis of Vaccination and Case Fatality in Mississippi, USA.

The COVID-19 pandemic has created a severe upheaval in the U.S., with a particular burden on the state of Mississippi, which already has an exhausted healthcare burden. The main objectives of this study are: (1) to analyze the county-level COVID-19 cases, deaths, and vaccine distribution and (2) to determine the correlation between various social determinants of health (SDOH) and COVID-19 vaccination coverage. We analyzed COVID-19-associated data and county-level SDOH factors in 82 counties of Mississippi. The cumulative COVID-19 and socio-demographic data variables were grouped into feature and target variables. The statistical and exploratory data analysis (EDA) was conducted using Python 3.8.5. The correlation between the target and feature variables was performed by Pearson Correlation analysis. The heat Map Correlation Matrix was visually presented to illustrate the correlation between each pair of features and each target variable. Results indicated that people of Asian descent had the highest vaccination coverage of 77% fully vaccinated compared to 52%, 46%, 42% and 25% for African Americans, Whites, Hispanics, and American Indians/Alaska Natives, respectively. The county-level vaccination rate was significantly higher among the minority populations than the White population. It was observed that COVID-19 cases and deaths were positively correlated with per capita income and negatively correlated with the percentage of persons without a high school diploma (age 25+). This study strongly demonstrates that different SDOH factors influence the outcome of the COVID-19 vaccination rate, which also affects the total number of COVID-19 cases and deaths. Vaccine promotion should be given to all populations regardless of race and ethnicity to achieve uniform acceptance. Therefore, statewide policy recommendations focusing on specific community needs should help achieve health equity in COVID-19 vaccination management.

Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA.

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (-0.22) and between humidity and COVID-19 incidence rate (-0.15). The linear regression model showed the model linear coefficients to be 0.92 and -1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

Pharmacological Effects of Selected Medicinal Plants and Vitamins Against COVID-19.

The coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). It is a serious disease that has caused multiple deaths in various countries in the world. Globally, as of May 23, 2021, the total confirmed cases of COVID-19 have reach 166,346,635 with a total of 3,449,117 deaths. Several recent scientific studies have shown that medicinal plants and vitamins can benefit and improve the health of COVID-19 patients. However, the benefits of medicinal plants and vitamins in the treatment of COVID-19 remain unproven. Therefore, the objective of this article is to expounds the benefits of using medicinal plants (Allium sativum, curcumin, Nigella sativa, Zingiber officitale) and vitamins (vitamin C and vitamin D) that possess the antiviral properties for the prevention and/or control of COVID-19. To reach our objective, we searched scientific databases of ongoing trials in the Centers for Disease Control and Prevention websites, PubMed Central, Medline databases, and Google Scholar websites. We also searched databases on World Health Organization International Clinical Trials Registry Platform to collect relevant papers. We found that all of the selected medicinal plants and vitamins possess antiviral activities, and their individual intake shows promise for the prevention and/or control of COVID-19. We conclude that, the selected medicinal plants and vitamins possess anti-viral properties that are more likely to prevent and/or disrupt the SARS-CoV-2 replication cycle, enhance the human immune system and promote good health.

The Research Centers in Minority Institutions (RCMI) Consortium: A Blueprint for Inclusive Excellence.

The Research Centers in Minority Institutions, (RCMI) Program was established by Congress to address the health research and training needs of minority populations, by preparing future generations of scientists at these institutions, with a track record of producing minority scholars in medicine, science, and technology. The RCMI Consortium consists of the RCMI Specialized Centers and a Coordinating Center (CC). The RCMI-CC leverages the scientific expertise, technologies, and innovations of RCMI Centers to accelerate the delivery of solutions to address health disparities in communities that are most impacted. There is increasing recognition that the gap in representation of racial/ethnic groups and women is perpetuated by institutional cultures lacking inclusion and equity. The objective of this work is to provide a framework for inclusive excellence by developing a systematic evaluation process with common data elements that can track the inter-linked goals of workforce diversity and health equity. At its core, the RCMI Program embodies the trinity of diversity, equity, and inclusion. We propose a realist evaluation framework and a logic model that integrates the institutional context to develop common data metrics for inclusive excellence. The RCMI-CC will collaborate with NIH-funded institutions and research consortia to disseminate and scale this model.

Response to the COVID-19 Epidemic: The Chinese Experience and Implications for Other Countries.

The ongoing outbreak of the novel coronavirus disease (COVID-19) that occurred in China is rapidly spreading globally. China's bond and strict containment measures have been proved (in practice) to significantly reduce the spread of the epidemic. This was obtained through the use of emergency control measures in the epidemic areas and the integration of resources from multiple systems, including business, community, technology, education, and transportation, across the country. In order to better understand how China has managed to reduce the public health and economic impacts of the COVID-19 epidemic, this editorial systematically reviews the specific measures for infection prevention and control of the disease. The best practices for COVID-19 eradication in China provide evidence-based strategies that could be replicated in other countries.