Understanding the Anticontagion Process and Reopening of China during COVID-19 via Coevolution Network of Epidemic and Awareness

Figure 3

Real data analysis and the impact of three key epidemic factors, i.e., population mobility within cities, population migration between cities, and awareness diffusion. Subfigure (a) demonstrates the comparison between the 2019 and 2020 population mobility index of each city with a fitted parameter of 0.96. Subfigure (b) displays that the simulated cumulative confirmed cases change along with the control measures of population mobility within cities. The abscissa indicates the scaling factors. That is, the actual volume is used as the standard to zoom in or out. We perform the global, local, and temporal controls on an actual basis, with scaling ratios ranging from 0.1 to 2.0. Subfigures (c) and (d) show the results of controlling the population migration between cities in the same way. Subfigure (e) reveals the impact of three model parameters affecting awareness diffusion. The grey dots record the value of each parameter used in the model. Subfigure (f) demonstrates the outcomes of applying temporal control to awareness diffusion. (a) Data of population mobiliy within cities. (b) Data of population migration between cities. (c) Parameter of awareness diffusion. (d) Impact of population mobility within cities. (e) Impact of population migration between cities. (f) Impact of awareness diffusion.

(a)

(b)

(c)

(d)

(e)

(f)