Early network properties of the COVID-19 pandemic – The Chinese scenario
Objectives: To control epidemics, sites more affected by mortality should be identified. Methods: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. Results: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity – network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I–III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I–III epidemic nodes were geo-temporally and statistically distinguishable. Conclusions: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:96 |
|---|---|
| Enthalten in: |
International Journal of Infectious Diseases - 96(2020), Seite 519-523 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ariel L. Rivas [VerfasserIn] |
|---|
| Links: |
doi.org [kostenfrei] |
|---|
| Themen: |
COVID-19 |
|---|
| doi: |
10.1016/j.ijid.2020.05.049 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
DOAJ008581533 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ008581533 | ||
| 003 | DE-627 | ||
| 005 | 20230503030048.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ijid.2020.05.049 |2 doi | |
| 035 | |a (DE-627)DOAJ008581533 | ||
| 035 | |a (DE-599)DOAJdfbd835c19f54f5b87fcbac6cc8cafa5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 050 | 0 | |a RC109-216 | |
| 100 | 0 | |a Ariel L. Rivas |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Early network properties of the COVID-19 pandemic – The Chinese scenario |
| 264 | 1 | |c 2020 | |
| 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: To control epidemics, sites more affected by mortality should be identified. Methods: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. Results: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity – network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I–III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I–III epidemic nodes were geo-temporally and statistically distinguishable. Conclusions: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians. | ||
| 650 | 4 | |a Network-theory | |
| 650 | 4 | |a Smallworld | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Interdisciplinary | |
| 650 | 4 | |a Geo-referenced | |
| 653 | 0 | |a Infectious and parasitic diseases | |
| 700 | 0 | |a José L. Febles |e verfasserin |4 aut | |
| 700 | 0 | |a Stephen D. Smith |e verfasserin |4 aut | |
| 700 | 0 | |a Almira L. Hoogesteijn |e verfasserin |4 aut | |
| 700 | 0 | |a George P. Tegos |e verfasserin |4 aut | |
| 700 | 0 | |a Folorunso O. Fasina |e verfasserin |4 aut | |
| 700 | 0 | |a James B. Hittner |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t International Journal of Infectious Diseases |d Elsevier, 2015 |g 96(2020), Seite 519-523 |w (DE-627)DOAJ000043923 |x 18783511 |7 nnns |
| 773 | 1 | 8 | |g volume:96 |g year:2020 |g pages:519-523 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.ijid.2020.05.049 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/dfbd835c19f54f5b87fcbac6cc8cafa5 |z kostenfrei |
| 856 | 4 | 0 | |u http://www.sciencedirect.com/science/article/pii/S1201971220303568 |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 | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |d 96 |j 2020 |h 519-523 | ||