Modeling the Effects of Traffic Reduction on the Severity of the COVID-19 Epidemic in U.S. States

<p>The purpose of this research was to determine the relationship between traffic movements and COVID-19 infections, and ultimately hospitalizations and deaths, throughout various U.S. States using the infection curve and equations from the Susceptible-Infected-Recovered (SIR) model.</p> <p>As a result of state and national governmental restrictions and public perception of the virus, traffic patterns were severely altered throughout the peak of the pandemic in 2020 and 2021. Traffic volumes experienced the greatest reduction when governmental restrictions were first enforced at the beginning of the pandemic and began to approach pre-pandemic values during 2021 as facilities throughout the country reopened. The prediction model applies the traffic volume conditions during the initial stage of the pandemic to the entire study period to determine the effect traffic volumes have on COVID-19 infections. Once the observed infection data were modeled, the adjusted, predicted model was determined using a series of modified SIR equations that reflect changes in traffic, and the findings suggest infection numbers may have been reduced compared to the observed data for each U.S. state studied. The number of hospitalizations and deaths that may be reduced during the second peak given the traffic conditions from the beginning of the pandemic were calculated based on the predicted model results for each state.</p> <p>The findings suggested by the predicted model, a reduction in infections, hospitalizations, and deaths, can benefit health service facilities in limiting the overcrowding and shortage of ventilators, which can result in fewer deaths caused by COVID-19. This research provides insights for practitioners, researchers, and government entities developing and accessing plans for future pandemics. It is also expected that the findings of this study can be built upon by future researchers who continue to study various aspects of the COVID-19 pandemic and assess the public response to governmental actions.</p>

Name thesis:degree_name

Master of Science in Civil Engineering

Level thesis:degree_level

Thesis - ERAU Login Required

Discipline thesis:degree_discipline

Civil Engineering

Year

2023

Author dc:creator

• Grant, Tate

• COVID-19
• United States
• Traffic
• Infections
• Exposure
• Epidemic
• Pandemic
• SIR Model
• Prediction
• Other Immunology and Infectious Disease
• Transportation Engineering

Repository record dc:identifier

https://commons.erau.edu/edt/737

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1741