Project Title

Spatiotemporal Analysis of the COVID-19 Pandemic in School-age Children (5-18 years) in Washington and Johnson County, TN

Authors' Affiliations

Omobolaji F. Olawuyi, Department of Geosciences, College of Arts and Sciences, East Tennessee State University, Johnson City, TN. Ingrid E. Luffman, Department of Geosciences, College of Arts and Sciences, East Tennessee State University, Johnson City, TN.

Location

Culp Ballroom

Start Date

4-7-2022 9:00 AM

End Date

4-7-2022 12:00 PM

Poster Number

74

Faculty Sponsor’s Department

Geosciences

Name of Project's Faculty Sponsor

Ingrid Luffman

Classification of First Author

Graduate Student-Master’s

Competition Type

Competitive

Type

Poster Presentation

Project's Category

Infectious Diseases

Abstract or Artist's Statement

Abstract

COVID-19, as named by the World Health Organization, is a disease caused by severe acute respiratory syndrome, coronavirus 2 (SARS CoV-2). This study is a spatiotemporal analysis of the COVID-19 pandemic in school-age children (5-18 years) in Washington and Johnson County, Tennessee and the possible relationship between public policies and the rate of infection. The first cases in Tennessee were documented in March 2020, with data being collected since that time. Daily data are accessible on the Tennessee Health Department COVID-19 dashboard with the number of new cases, hospitalizations, and deaths grouped by county in ages 5-11 years and 12-18 years. As this disease spread, government officials mandated different policies: mask mandates, stay at home, restrictions of public gatherings, and school closure, but many schools eventually allowed physical attendance. Emerging spatiotemporal hotspots are analyzed to identify the spatial clustering patterns of hot and cold spots with statistical significance using the Moran I statistical model in ArcGIS. The Change point detection tool in ArcGIS makes inferences about significant changes in trends over time; it was used to identify when significant changes occur. This is an ongoing project that will inform the approach I will adopt for my thesis, statistical tools will be used to determine the correlation between the time the change occurred and the implementation of policies, with an estimated 14-day lag time. Finally, the findings from both age groups will be compared. This study aims to help policymakers make better-informed decisions when responding to future pandemics.

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Apr 7th, 9:00 AM Apr 7th, 12:00 PM

Spatiotemporal Analysis of the COVID-19 Pandemic in School-age Children (5-18 years) in Washington and Johnson County, TN

Culp Ballroom

Abstract

COVID-19, as named by the World Health Organization, is a disease caused by severe acute respiratory syndrome, coronavirus 2 (SARS CoV-2). This study is a spatiotemporal analysis of the COVID-19 pandemic in school-age children (5-18 years) in Washington and Johnson County, Tennessee and the possible relationship between public policies and the rate of infection. The first cases in Tennessee were documented in March 2020, with data being collected since that time. Daily data are accessible on the Tennessee Health Department COVID-19 dashboard with the number of new cases, hospitalizations, and deaths grouped by county in ages 5-11 years and 12-18 years. As this disease spread, government officials mandated different policies: mask mandates, stay at home, restrictions of public gatherings, and school closure, but many schools eventually allowed physical attendance. Emerging spatiotemporal hotspots are analyzed to identify the spatial clustering patterns of hot and cold spots with statistical significance using the Moran I statistical model in ArcGIS. The Change point detection tool in ArcGIS makes inferences about significant changes in trends over time; it was used to identify when significant changes occur. This is an ongoing project that will inform the approach I will adopt for my thesis, statistical tools will be used to determine the correlation between the time the change occurred and the implementation of policies, with an estimated 14-day lag time. Finally, the findings from both age groups will be compared. This study aims to help policymakers make better-informed decisions when responding to future pandemics.