SARS-COV-2 DISSEMINATION USING UNITED STATES COUNTY ADJACENCIES

Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is increasing amongst the world’s population at an alarming rate. Reducing the spread of SARS-CoV-2 is paramount for public health officials as they seek to effectively manage resources and potential population control measures such as social distancing and quarantine. By analyzing the United States’ county network structure, one can model and interdict potential higher infection areas. County officials can provide targeted information, preparedness training, and increased testing in these areas. While these approaches may provide adequate countermeasures for localized areas, they are inadequate for the holistic United States. We solve this problem by collecting data on coronavirus-19 (COVID-19) infections and deaths from the Center for Disease Control and Prevention and a network adjacency structure from the United States Census Bureau. Generalized network autoregressive (GNAR) time series models have been proposed as an efficient learning algorithm for networked datasets. This thesis fuses network science and operations research techniques to univariately model COVID-19 cases, deaths, and current survivors across the United States’ county network structure.

Department dc:contributor.department

Operations Research (OR)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2021

Author dc:creator

• Wren, David M.

Advisors dc:contributor.advisor

• Yoshida, Ruriko
• Vogiatzis, Chrysafis, University of Illinois

Statement dc:rights

• This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.

Handle dc:identifier.uri

https://hdl.handle.net/10945/67833

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/67833