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
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
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.