Early network properties of the COVID-19 pandemic – The Chinese scenario

• Published in: International journal of infectious diseases 2020-07, Vol.96, p.519-523
• Main Authors: Rivas, Ariel L., Febles, José L., Smith, Stephen D., Hoogesteijn, Almira L., Tegos, George P., Fasina, Folorunso O., Hittner, James B.
• Format: Article
• Language: English
• Subjects: Betacoronavirus; China - epidemiology; Coronavirus Infections - epidemiology; Coronavirus Infections - mortality; COVID-19; Geo-referenced; Humans; Interdisciplinary; Logistic Models; Network-theory; Pandemics; Pneumonia, Viral - epidemiology; Pneumonia, Viral - mortality; SARS-CoV-2; Smallworld; Spatio-Temporal Analysis
• ISSN: 1201-9712; 1878-3511
• DOI: 10.1016/j.ijid.2020.05.049

•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. To control epidemics, sites more affected by mortality should be identified. 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. 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. 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.