Transmission dynamics and optimal control of brucellosis in Inner Mongolia of China

Figure 1.  Schematic transmission diagram of brucellosis among sheep and two human subpopulations. $S, I, V$ represent susceptible, infectious and vaccinated sheep. $S_1$, $I_1$, $C_1$ and $S_2$, $I_2$, $C_2$ represent susceptible, acute cases and chronic cases of high risk human subpopulation $G_1$ and low risk human subpopulation $G_2$, respectively

Figure 2.  Predicted tendency of brucellosis in Inner Mongolia. The solid line represents the prediction of (1) and the diamonds are the reported data in Inner Mongolia of P. R. China. (a) Number of brucellosis sheep ($I$). (b) Total number of new confirmed human cases ($I_h^{NA}=I_1^{NA}+I_2^{NA}$). (c) Number of new confirmed human cases of high risk subpopulation $G_1$ ($I_1^{NA}$). (d) Number of new confirmed human cases of low risk subpopulation $G_2$ ($I_2^{NA}$). Here, the culling rate $k=0.05$ from 2001 to 2011 [41], $k=0.5$ from 2012 to 2015 [17,35,40], $k=0.15$ after 2015, $\delta=0.67$, $\theta=0.6$, $q_1=q_2=0.4$, other parameter values are listed in Table 1

Figure 3.  Influence of vaccination ($\theta$). The vaccination of sheep can reduce the epidemic situation of brucellosis but can not eliminate it. The values of parameters are the same as those in Fig. 2 except $\theta$

Figure 4.  Influence of the recruitment of sheep ($A$). It shows that reducing the size of sheep population in Inner Mongolia could assert great positive effect on brucellosis control. The values of parameters are the same as those in Fig. 2 except $A$

Figure 5.  Influence of culling of the infected sheep ($k$). Increasing the culling rate of infected sheep could effectively reduce the incidence of brucellosis in both sheep and human and can even eradicate the brucellosis. The values of parameters are the same as those in Fig. 2 except $k$

Figure 6.  Influence of the transmission rate between brucellosis sheep and human($\beta$). Compared to Fig. 4 and Fig. 5, it illustrates that reducing the brucellosis transmission rate between sheep and human has lower efficacy than the controls carried out in sheep stock, since it can not control the source of disease transmission. Here, $\beta_0=4.895\times 10^{-6}$, other values of parameters are the same as those in Fig. 2

Figure 7.  The optimal control $\theta^*(t)$, $\varphi_1^*(t)$, $\varphi_2^*(t)$ and the corresponding $I^*(t)$, $I_1^*(t)$, $I_2^*(t)$. The dot-dash curves ($\omega_1=\omega_2=0, \omega_3=\omega_4=0.5$) depict the trends of brucellosis without vaccination programme in sheep stock and health education in human. The dash curves ($\omega_1=\omega_2=0.5, \omega_3=\omega_4=0$) illustrate that the disease can not be eliminated eventually even if the control resources supply is maximal. The solid curves ($\omega_1=\omega_2=\omega_3=0.3,\omega_4=0.1$) show that the situation of brucellosis epidemic will be serious if the control resources supply is limited. Here, $A=3300$, $k=0.15$, $T=34$, $\bar{\theta}=0.85$, $\bar{\varphi_1}=\bar{\varphi_2}=0.9$, and $B_0=0.2$, $B_1=0.3$, $B_2=0.2$, $D_0=1$, $D_1=5$, $D_2=3$, other parameter values are the same as those in Fig. 2

Figure 8.  Simulation of the optimal control $\theta^*(t)$, $\varphi_1^*(t)$, $\varphi_2^*(t)$ with varying weighted coefficients. It depicts the significant effect of the weighted coefficients on the optimal control. When $\omega_1$ increase from $0$ to $0.5$, the key minimizing targets switch from vaccination and health education to culling of brucellosis sheep and treatment of human cases, and, accordingly, the optimal control gradually increases from $\theta^*(t)=\varphi_1^*(t)=\varphi_2^*(t)=0$ to $\theta^*(t)=\overline{\theta}$, $\varphi_1^*(t)=\overline{\varphi}_1$ and $\varphi_2^*(t)=\overline{\varphi}_2$. Here $\omega_1=\omega_2, \omega_3=\omega_4$, and $\omega_1+\omega_3=0.5$, other parameter values are the same as those in Fig. 7

Figure 9.  Total economic loss (i.e., the sum of the four integrals in (12)) and prevalence rates vary with the weighted coefficients. The policy-maker should confirm the weight coefficients according to the financial budget for brucellosis ($TELoss^*$) and the prevalence rates in both sheep and human population ($P_s^*,P_{h1}^*,P_{h2}^*$). Here $\omega_1=\omega_2, \omega_3=\omega_4$, and $\omega_1+\omega_3=0.5$, other parameter values are the same as those in Fig. 7

Figure 10.  Simulations of the optimal control $\theta^*(t)$, $\varphi_1^*(t)$, $\varphi_2^*(t)$ and the corresponding $I^*(t)$, $I_1^*(t)$, $I_2^*(t)$, with the culling rate $k=0.15$, $k=0.3$ and $k=0.7$, respectively. It reveals the fact again that culling of infected sheep plays an important role on brucellosis control. Here, $\omega_1=\omega_2=\omega_3=0.3, \omega_4=0.1$, other parameter values are the same as those in Fig. 7

Figure 11.  Simulations of the optimal control $\theta^*(t)$, $\varphi_1^*(t)$, $\varphi_2^*(t)$ and the corresponding $I^*(t)$, $I_1^*(t)$, $I_2^*(t)$ with constant recruitment rate of sheep $A=3300$, $A=2200$ and $A=1600$, respectively. Compared to Fig. 7, the brucellosis epidemic situations are effectively controlled in both sheep and human when the constant recruitment rate of sheep $A$ is decreased from $A=3300$ to $A=1600$. It illustrates that reducing size of sheep population in Inner Mongolia is also an effective control measure. Here, $\omega_1=\omega_2=\omega_3=0.3, \omega_4=0.1$, other parameter values are the same as those in Fig. 7