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Identifying the shifting sources to predict the dynamics of COVID-19 in the U.S.
Wang, Yanchao; Zhong, Lu; Du, Jing; Gao, Jianxi; Wang, Qi.
  • Wang Y; Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, USA.
  • Zhong L; Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th St., Troy, New York 12180, USA.
  • Du J; Department of Civil and Coastal Engineering, University of Florida, 460F Weil Hall, Gainesville, Florida 32611, USA.
  • Gao J; Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th St., Troy, New York 12180, USA.
  • Wang Q; Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, USA.
Chaos ; 32(3): 033104, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1747110
ABSTRACT
Mobility restriction is a crucial measure to control the transmission of the COVID-19. Research has shown that effective distance measured by the number of travelers instead of physical distance can capture and predict the transmission of the deadly virus. However, these efforts have been limited mainly to a single source of disease. Also, they have not been tested on finer spatial scales. Based on prior work of effective distances on the country level, we propose the multiple-source effective distance, a metric that captures the distance for the virus to propagate through the mobility network on the county level in the U.S. Then, we estimate how the change in the number of sources impacts the global mobility rate. Based on the findings, a new method is proposed to locate sources and estimate the arrival time of the virus. The new metric outperforms the original single-source effective distance in predicting the arrival time. Last, we select two potential sources and quantify the arrival time delay caused by the national emergency declaration. In doing so, we provide quantitative answers on the effectiveness of the national emergency declaration.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Chaos Journal subject: Science Year: 2022 Document Type: Article Affiliation country: 5.0051661

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Chaos Journal subject: Science Year: 2022 Document Type: Article Affiliation country: 5.0051661