Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks<xref rid="cpb_28_12_128901_fn1" ref-type="fn">*</xref><fn id="cpb_28_12_128901_fn1"><label>*</label><p>Project supported by the National Key R&D Program of China (Grant No. 2018YFF0301005), the National Natural Science Foundation of China (Grant Nos. 71673161 and 71790613), and the Collaborative Innovation Center of Public Safety, China.</p></fn>

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks**

Project supported by the National Key R&D Program of China (Grant No. 2018YFF0301005), the National Natural Science Foundation of China (Grant Nos. 71673161 and 71790613), and the Collaborative Innovation Center of Public Safety, China.

Ma Xun^{1, 2, 3}, Cui Ya-Peng^{1, 2, 3}, Yan Xiao-Li^{1, 2, 3}, Ni Shun-Jiang^{1, 2, 3, †}, Shen Shi-Fei^{1, 2, 3}

C(τ) and τ of the three strategies versus M_s with different screening range and p_s from 0.1 to 0.9 under different μ. (a) Strategy I with N_s = 6,18,30,42 under μ = −2. (b) Strategy I with N_s = 6,18,30,42 under μ = 2. (c) Strategy II with N_s = 6,18,30,42 under μ = −2. (d) Strategy II with N_s = 6,18,30,42 under μ = 2. (e) Strategy III with d_c = 5,20,35,50 under μ = −2. (f) Strategy III with d_c = 5,20,35,50 under μ = 2. The solid points represent C(τ) and hollow points represent τ. Each point is generated from 500 simulations of global infection.