Pandemic risk of COVID-19 outbreak in the United States: An analysis of network connectedness with air travel data
Objectives: The United States has become the country with the largest number of COVID-19 reported cases and deaths. This study aims to analyze the pandemic risk of COVID-19 outbreak in the US. Methods: Time series plots of the network density, together with the daily reported confirmed COVID-19 cases and flight frequency in the five states in the US with the largest numbers of COVID-19 cases were developed to discover the trends and patterns of the pandemic connectedness of COVID-19 among the five states. Results: The research findings suggest that the pandemic risk of the outbreak in the US could be detected as early as the beginning of March. The signal was prior to the rapid increase of reported COVID-19 cases and flight reduction measures. Travel restriction can be strengthened at an early stage of the outbreak while more focus of local public health measures can be addressed after community spread. Conclusions: The study demonstrates the application of network density on detection of pandemic risk and its relationship with air travel restriction in order to provide useful information for policymakers to better optimize timely containment strategies to mitigate the outbreak of infectious diseases..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:103 |
|---|---|
| Enthalten in: |
International Journal of Infectious Diseases - 103(2021), Seite 97-101 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Agnes Tiwari [VerfasserIn] |
|---|
| Links: |
doi.org [kostenfrei] |
|---|
| Themen: |
Air traffic |
|---|
| doi: |
10.1016/j.ijid.2020.11.143 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
DOAJ017153379 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ017153379 | ||
| 003 | DE-627 | ||
| 005 | 20230310085754.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230226s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ijid.2020.11.143 |2 doi | |
| 035 | |a (DE-627)DOAJ017153379 | ||
| 035 | |a (DE-599)DOAJd1a7e09fd20b4d03a64ae927310731ab | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 050 | 0 | |a RC109-216 | |
| 100 | 0 | |a Agnes Tiwari |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Pandemic risk of COVID-19 outbreak in the United States: An analysis of network connectedness with air travel data |
| 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 Objectives: The United States has become the country with the largest number of COVID-19 reported cases and deaths. This study aims to analyze the pandemic risk of COVID-19 outbreak in the US. Methods: Time series plots of the network density, together with the daily reported confirmed COVID-19 cases and flight frequency in the five states in the US with the largest numbers of COVID-19 cases were developed to discover the trends and patterns of the pandemic connectedness of COVID-19 among the five states. Results: The research findings suggest that the pandemic risk of the outbreak in the US could be detected as early as the beginning of March. The signal was prior to the rapid increase of reported COVID-19 cases and flight reduction measures. Travel restriction can be strengthened at an early stage of the outbreak while more focus of local public health measures can be addressed after community spread. Conclusions: The study demonstrates the application of network density on detection of pandemic risk and its relationship with air travel restriction in order to provide useful information for policymakers to better optimize timely containment strategies to mitigate the outbreak of infectious diseases. | ||
| 650 | 4 | |a Air traffic | |
| 650 | 4 | |a Community transmission | |
| 650 | 4 | |a Coronavirus | |
| 650 | 4 | |a Network analysis | |
| 650 | 4 | |a Pandemic connectedness | |
| 653 | 0 | |a Infectious and parasitic diseases | |
| 700 | 0 | |a Mike K.P. So |e verfasserin |4 aut | |
| 700 | 0 | |a Andy C.Y. Chong |e verfasserin |4 aut | |
| 700 | 0 | |a Jacky N.L. Chan |e verfasserin |4 aut | |
| 700 | 0 | |a Amanda M.Y. Chu |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t International Journal of Infectious Diseases |d Elsevier, 2015 |g 103(2021), Seite 97-101 |w (DE-627)DOAJ000043923 |x 18783511 |7 nnns |
| 773 | 1 | 8 | |g volume:103 |g year:2021 |g pages:97-101 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.ijid.2020.11.143 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/d1a7e09fd20b4d03a64ae927310731ab |z kostenfrei |
| 856 | 4 | 0 | |u http://www.sciencedirect.com/science/article/pii/S1201971220324541 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1201-9712 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 103 |j 2021 |h 97-101 | ||