ABSTRACT
The Coronavirus disease 2019 (COVID-19) pan-demic has severely impacted countries around the world with unprecedented mortality and economic devastation and has dis-proportionately and negatively impacted different communities-especially racial and ethnic minorities who are at a particular disadvantage as they are more likely to be the potential target of COVID-19 infection. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information to trace the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose GMLTrace, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracing with a goal to improve pandemic health care. © 2021 IEEE.
ABSTRACT
Coronavirus disease 2019 (COVID-19) is a new disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global pandemic that has claimed the death of 1,536,957 human beings worldwide including 287,842 deaths in the United States as of December 3, 2020. It has become a major threat to the medical community and the entire healthcare system in every part of the world. Recently, the Food and Drug Administration (FDA) has approved the emergency use of Pfizer and Moderna COVID-19 vaccine on December 12, 2020. However, there are concern about the new COVID-19 vaccine safety, efficacy, and immunity after the vaccination. In addition, both coronavirus and COVID-19 vaccine are new at this point and there is no scientific evidence to know whether people who are vaccinated can still carry the COVID 19 pathogens and pass them along to others. Therefore, many people all over the world have an increased interest in consuming more VF for the purpose of maintaining their health and boosting their immune system. Identifying novel antiviral agents for COVID-19 is of critical importance, and VF is an excellent source for drug discovery and therapeutic development. The objective of this study is to test the hypothesis that a high intake of vegetables and/or fruits prevents COVID-19 incidence and reduces the mortality rate. To achieve this objective, we collected the diet data of COVID-19 from Kaggle (https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset), and used a machine-learning algorithm to examine the effects of different food types on COVID-19 incidences and deaths. Specifically, we used the feature selection method to identify the factors (e.g., diet-related factors) that contribute to COVID-19 morbidity and mortality. Data generated from the study demonstrated that VF intake can help to combat the SARS-CoV-2. Taken together, VF may be potential chemopreventive agents for COVID-19 due to their antiviral properties and their ability to boost the human body immune system.