Early network properties of the COVID-19 pandemic – The Chinese scenario
| Autor: | José L. Febles, Almira L. Hoogesteijn, Stephen D. Smith, James B. Hittner, Folorunso Oludayo Fasina, George P. Tegos, Ariel L. Rivas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Microbiology (medical) China Best fitting Coronavirus disease 2019 (COVID-19) 030106 microbiology Pneumonia Viral Logistic regression Article lcsh:Infectious and parasitic diseases 03 medical and health sciences Betacoronavirus 0302 clinical medicine Spatio-Temporal Analysis Interdisciplinary Geo-referenced Statistics Pandemic Node (computer science) Rank (graph theory) Humans lcsh:RC109-216 030212 general & internal medicine Pandemics Unit of time SARS-CoV-2 Network-theory Assortativity COVID-19 General Medicine Geography Logistic Models Infectious Diseases Smallworld Coronavirus Infections |
| Zdroj: | International Journal of Infectious Diseases International Journal of Infectious Diseases, Vol 96, Iss, Pp 519-523 (2020) |
| ISSN: | 1201-9712 |
| DOI: | 10.1016/j.ijid.2020.05.049 |
| Popis: | Highlights • Classic epidemiological control programs assume that the population is homogeneously distributed in geographical areas also regarded to be homogeneous. • Following Network Theory and considering that neither the population nor the geography are homogeneous, the geo-temporal progression of COVID-19 was explored with the data collected in China. • Several network properties, including synchronicity and directionality, were observed, which distinguished the epidemic profiles observed in several provinces. • This real-time analysis fosters policies tailored to specific geo-biological situations. 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. |
| Databáze: | OpenAIRE |
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