Background Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Methods and findings We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. Conclusions The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).

No abstract

Background: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Method: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Findings: Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 30% in daily cases. Interpretation: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available). Funding: None.

Dr. Zaurin obtained his Bachelor Degree in Civil Engineering from 'Universidad de Oriente' in Venezuela in 1985. In 1990 he earned a MSc in Information Technology. He has been civil engineering professor with teaching experience at his Alma Mater (Universidad de Oriente) from 1986 until 2002. Dr. Zaurin moves to USA and completes another MSc, this time Structural and Geotechnical Engineering. Upon completing multidisciplinary PhD on Structural Health Monitoring Using Computer Vision, he joined UCF in 2010 as a Lecturer at the Civil, Environmental and Construction Engineering (CECE) Department. He has published computer vision related research work in prominent journals and still mentors graduate students in this particular area. Dr. Zaurin has been very active in the STEM area as he is one of the selected faculty members for the NSF funded EXCEL and NSF funded COMPASS programs at UCF. Dr. Zaurin received College Excellence in Undergraduate Teaching Award in 2015 and 2019, TIP Award in 2016, and also received 4 Golden Apple Awards for Undergraduate Teaching for a record four years in a row. During Fall 2013 he created IDEAS (Interdisciplinary Display for Engineering Analysis Statics)which is a project based learning activity designed specifically for promoting creativity, team-work, and presentation skills for undergraduate sophomore and junior students, as well as by exposing the students to the fascinating world of scientific/technological research based engineering. IDEAS is becoming the cornerstone event for the sophomore engineering students at UCF: from fall 2013 to fall 2018 approximately 3000 students have created, designed, presented, and defended around 900 projects and papers.

No abstract