Individual based models and differential equations models of nosocomial epidemics in hospital intensive care units

Figure 1.  Schematic diagram of the IBM patient compartments and parameters. All exiting patients are replaced immediately by an uninfected patient

Figure 2.  An example of the IBM with parameters $N_H = 4$ $N_P$ = 10, $T_V = 4 hr$ $N_V = 2$, $\omega_{Noff}=0.9$, $\omega_{Non}=0.9$, $\omega_R=0.9$, $\pi_N=0.9$, $\pi_R=0.9$, $\beta_{on}=0.6$, $\beta_{off}=0.6$, $\mu_{Noff}=0.25$, $\mu_{on}=0.2$, $\mu_R=0.1667$

Figure 3.  Example 2 of the IBM with parameters $N_H = 10, $ $N_P = 30$, $N_V = 16$, $\omega_{Noff}=0.1$, $\omega_{Non}=0.1$, $\omega_R=0.2$, $\pi_N=0.1$, $\pi_R=0.2$, $\beta_{on}=0.3$, $\beta_{off}=0.1$, $\mu_{Noff}=0.25$, $\mu_{on}=0.2$, $\mu_R=0.1667$. All three infected patient compartments extinguish

Figure 4.  Graphs of 50 runs of the IBM with parameters and initial conditions in Example 2. The thicker curves represent the averages of the 50 runs in each compartment. The averages of the simulations approach extinction in 30 days, but some individual runs do not

Figure 5.  Example 3 of the IBM with parameters $N_H = 10, $ $N_P = 30$, $N_V = 16$, $\omega_{Noff}=0.3$, $\omega_{Non}=0.1$, $\omega_R=0.2$, $\pi_N=0.2$, $\pi_R=0.2$, $\beta_{on}=0.4$, $\beta_{off}=0.1$, $\mu_{Noff}=0.25$, $\mu_{on}=0.2$, $\mu_R=0.1667$. Only the resistant strain is prevalent after 30 days

Figure 6.  Graphs of 50 runs of the IBM with parameters and initial conditions in Example 3. The thicker curves represent the averages of the 50 runs in each compartment. The average of the resistant patient populations approaches extinction over 30 days, but the averages of the nonresistant patient populations do not

Figure 7.  Example 4 of the IBM with parameters $N_H = 10, $ $N_P = 30$, $N_V = 16$, $\omega_{Noff}=0.2$, $\omega_{Non}=0.2$, $\omega_R=0.2$, $\pi_N=0.2$, $\pi_R=0.3$, $\beta_{on}=0.7$, $\beta_{off}=0.03$, $\mu_{Noff}=0.25$, $\mu_{on}=0.2$, $\mu_R=0.1$. Both the nonresistant and resistant strains are prevalent after 30 days

Figure 8.  Graphs of 50 runs of the IBM with parameters and initial conditions in Example 4. The thicker curves represent the averages of the 50 runs in each compartment. The averages of both nonresistant and resistant strains are prevalent after 30 days

Figure 9.  The DEM in the case that both strains extinguish. The parameters match the parameters for the IBM in Example 2: $N_P=30$, $N_H=10$, $N_V=16$, $\omega_{Noff}=0.1$, $\omega_{Non}=0.1$, $\omega_R=0.2$, $\mu_{Noff}=0.25=1/T_{Noff}$, $\mu_{Non}=0.2=1/T_{Non}$, $\mu_R=0.1667=1/T_R$, $\pi_N=0.1$, $\pi_R=0.2$, $\beta_{on}=0.3$, $\beta_{off}=0.1$

Figure 10.  Phase portrait of the DEM trajectories $(PN_{off}(t), PN_{on}(t), PR(t))$ for an array of initial conditions with the parameters in Example 5. The thick black curve corresponds to the graphs in Fig. 9. Trajectories oscillate as they converge to $(0, 0, 0)$. An initial increase or decrease in infected patient populations could be misinterpreted as a long-term trend

Figure 11.  The DEM in the case that only the nonresistant strain extinguishes. The parameters match the parameters for the IBM in Example 3: $N_P=30$, $N_H=10$, $N_V=16$, $\omega_{Noff}=0.3$, $\omega_{Non}=0.1$, $\omega_R=0.2$, $\mu_{Noff}=0.25=1/T_{Noff}$, $\mu_{Non}=0.2=1/T_{Non}$, $\mu_R=0.1667=1/T_R$, $\pi_N=0.2$, $\pi_R=0.2$, $\beta_{on}=0.4$, $\beta_{off}=0.1$

Figure 12.  Phase portrait of the DEM trajectories $(PN_{off}(t), PN_{on}(t), PR(t))$ for an array of initial conditions with the parameters in Example 6. The thick black curve corresponds to the graphs in Fig. 11. All trajectories converge to $(5.56098, 11.122, 0)$

Figure 13.  The DEM in the case that both strains become endemic. The parameters match the parameters for the IBM in Example 4: $N_P=30$, $N_H=10$, $N_V=16$, $\omega_{Noff}=0.2$, $\omega_{Non}=0.2$, $\omega_R=0.2$, $\mu_{Noff}=0.25=1/T_{Noff}$, $\mu_{Non}=0.2=1/T_{Non}$, $\mu_R=0.1=1/T_R$, $\pi_N=0.2$, $\pi_R=0.3$, $\beta_{on}=0.7$, $\beta_{off}=0.03$

Figure 14.  Phase portrait of the DEM trajectories $(PN_{off}(t), PN_{on}(t), PR(t))$ for an array of initial conditions with the parameters in Example 7. The thick black curve corresponds to the graphs in Fig. 13. All trajectories converge with oscillations to the same limiting value

Figure 15.  Graph of $R_{01}$ as a function of $\beta_{off}$ and $\pi_R$. All other parameters are as in Example 7. $R_{01}$ increases linearly with increasing $\pi_R$, but nonlinearly with increasing $\beta_{off}$. Consequently, there is advantage in stopping AB use in patients infected with the resistant strain as soon as possible. The yellow dot corresponds to the parameters in Example 7. The red plane corresponds to $R_{01}=1.0$

Figure 16.  Graph of $R_{01}$ as a function of $T_{Non}$ and $T_R$. All other parameters are as in Example 7. $R_{01}$ increases sub-linearly with increasing $T_{Non}$ and $T_R$. Consequently, there is greater effect in reducing the LOS of patients infected with the nonresistant strain on AB and patients infected with the resistant strain (also on AB) as soon as possible. The yellow dot corresponds to the parameters in Example 7. The red plane corresponds to $R_{01}=1.0$

Figure 17.  Graph of $R_{01}$ as a function of $T_V / 48$ days = visit time intervals in hours and $N_P$. All other parameters are as in Example 7. $R_{01}$ increases super-linearly with decreasing $T_V$ and $N_P$. Increasing either $T_V$ or $N_P$ results in fewer patient-HCW visits. The yellow dot corresponds to the parameters in Example 7. The red plane corresponds to $R_{01}=1.0$