SARS-CoV-2 Dissemination using a Network of the United States Counties
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..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
arXiv.org - (2021) vom: 26. Nov. Zur Gesamtaufnahme - year:2021 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Urrutia, Patrick [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Förderinstitution / Projekttitel: |
|
|---|
| PPN (Katalog-ID): |
XAR033104840 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XAR033104840 | ||
| 003 | DE-627 | ||
| 005 | 20230429070231.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 211130s2021 xx |||||o 00| ||eng c | ||
| 035 | |a (DE-627)XAR033104840 | ||
| 035 | |a (arXiv)2111.13723 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 000 |q DE-84 | |
| 082 | 0 | |a 530 |q DE-84 | |
| 100 | 1 | |a Urrutia, Patrick |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a SARS-CoV-2 Dissemination using a Network of the United States Counties |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 700 | 1 | |a Wren, David |e verfasserin |4 aut | |
| 700 | 1 | |a Vogiatzis, Chrysafis |e verfasserin |4 aut | |
| 700 | 1 | |a Yoshida, Ruriko |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t arXiv.org |g (2021) vom: 26. Nov. |
| 773 | 1 | 8 | |g year:2021 |g day:26 |g month:11 |
| 856 | 4 | 0 | |u https://arxiv.org/abs/2111.13723 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XAR | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2021 |b 26 |c 11 | ||
| 953 | |2 045F |a 000 | ||
| 953 | |2 045F |a 530 | ||