BACKGROUND The health and wellness of every individual is determined by many aspects, including their genetics, environment, behaviors (both the individual and those around them), medical care, microbiome, and social factors affecting their life. In order to fully assess and fix and global health issue such as the COVID-19 Pandemic, analysis into each of these aspects is necessary. The root of every outbreak lies in one of these issues, and while fixing one may stop the spread of disease, it will never truly eradicate it. METHODOLOGY In a correlative, qualitative analysis, we obtained data on COVID-19 density in Worcester, Massachusetts from the city’s Department of Public Health. We overlayed the map associated with this data with heat maps on social factors in Worcester. These social factors included different metrics for areas like housing, nutrition, income, crime, transportation, and education. We analyzed these maps based on color and opacity to determine where social factors affecting health fell in relation to COVID-19 density; we also analyzed the numbers associated with the colors/opacities to show quantitatively how areas with a high prevalence of particular social factors vs. low matched up with COVID-19 density. Following this, a correlative machine-learning algorithm was developed to understand how members of the microbiome correlate to respiratory distress following recovery from COVID-19. Patients with COVID-19 or those exhibiting symptoms had a nasopharyngeal swab ordered upon admission to the UMass Memorial Hospital Emergency Department. Following this, patients were prompted by research staff to enroll in the study. To map the microbiota of each participant, whole DNA/RNA extraction was carried out, followed by Illumina sequencing. Illumina reads generated were to build a model for determining differences in microbial abundance as a predictor of respiratory support. The model was used to generate median importance values for a series of clinical factors, and microbial features found in the patients. To discern how specific microbes predicted the need for respiratory support or not, the model was also used to measure differences in abundancy levels of each microbe. RESULTS Analysis of the social determinants of health related to COVID-19 prevalence in Worcester revealed that all metrics were at least somewhat correlated. The best correlation existed between metrics like population density, income, and education. Slightly less significant, and likely less correlated were nutrition, and crime. Following this, data from the first model revealed that differences in microbial species’ abundances may act as better health predictors than age, BMI, and CCI when assessing if a patient will need respiratory support or not following COVID-19 recovery. Data from the second model revealed that abundances of microbes was highest in those patients who did not end up needing respiratory support DISCUSSION Since many of the social determinants are correlated with COVID-19 density, we have determined that further analyses into each factor is necessary to identify causal factors. Additionally, this evidence provides reason for the Worcester Department of Public Health to pursue campaigns that are geared towards preventing infections and assessing issues surrounding poverty, education, nutrition, and other social factors troubling particular populations in Worcester. In addition to this, data from the microbiology models shows us that many different microbes may actually exhibit protective attributes in the nasopharynx; the microbes in those patients who had high abundancies and no respiratory distress may interact with the immune system or fill in niches that would otherwise be filled with opportunistic pathogens. Similar to the former half of the project, more analyses must be performed to make better sense of the data.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Tocci, Christopher W.

Subject

• Health
• Well-Being

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-050621-164414
• 23311

Keyword

• Microbiology
• Epidemiology
• COVID-19
• Public Health
• SARS-CoV-2
• Pandemic

Advisor

• Faber, Brenton
• Rao, Reeta Prusty

Year

• 2021

Sponsor

• UMass Memorial Medical School

UN Sustainable Development Goals

• 3 - Good Health and Well-being

Date created

• 2021-05-06

Resource type

• Major Qualifying Project

Major

• Biology & Biotechnology

Rights statement

• In Copyright

Last modified

• 2022-05-16