  During 2020 and 2021, severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) transmission has been increasing amongst the world's population at
an alarming rate. Reducing the spread of SARS-CoV-2 and other diseases that are
spread in similar manners is paramount for public health officials as they seek
to effectively manage resources and potential population control measures such
as social distancing and quarantines. 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, as well as increase 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 coronavirus
disease 2019 (COVID-19) infections and deaths from the Center for Disease
Control and Prevention{\color{black},} and {\color{black} adjacency between all
counties obtained} 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 work fuses network science and
operations research techniques to univariately model COVID-19 cases, deaths,
and current survivors across the United States' county network structure.
