Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12552 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914649126993920 |
|---|---|
| author | Dong, Jiarui Ran, Guanghao |
| author_facet | Dong, Jiarui Ran, Guanghao |
| contents | In this study, we construct a series of evolving epidemic networks by measuring the correlations of daily COVID-19 cases time series among 3,105 counties in the United States. Remarkably, through quantitative analysis of the spatial distribution of these entities in different networks, we identify four typical patterns of COVID-19 transmission in the United States from March 2020 to February 2023. The onsets and wanes of these patterns are closely associated with significant events in the COVID-19 timeline. Furthermore, we conduct in-depth qualitative and quantitative research on the spread of the epidemic at the county and state levels, tracing and analyzing the evolution and characteristics of specific propagation pathways. Overall, our research breaks away from traditional infectious disease models and provides a macroscopic perspective on the evolution in epidemic transmission patterns. This highlights the remarkable potential of utilizing complex network methods for macroscopic studies of infectious diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12552 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Network Based Approach Estimating COVID-19 Spread Patterns Dong, Jiarui Ran, Guanghao Physics and Society In this study, we construct a series of evolving epidemic networks by measuring the correlations of daily COVID-19 cases time series among 3,105 counties in the United States. Remarkably, through quantitative analysis of the spatial distribution of these entities in different networks, we identify four typical patterns of COVID-19 transmission in the United States from March 2020 to February 2023. The onsets and wanes of these patterns are closely associated with significant events in the COVID-19 timeline. Furthermore, we conduct in-depth qualitative and quantitative research on the spread of the epidemic at the county and state levels, tracing and analyzing the evolution and characteristics of specific propagation pathways. Overall, our research breaks away from traditional infectious disease models and provides a macroscopic perspective on the evolution in epidemic transmission patterns. This highlights the remarkable potential of utilizing complex network methods for macroscopic studies of infectious diseases. |
| title | Network Based Approach Estimating COVID-19 Spread Patterns |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2401.12552 |