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Articles

Local approximation of Markov chains in time and space

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Pages 265-287 | Received 12 Feb 2018, Accepted 05 Jan 2019, Published online: 24 Jan 2019

Figures & data

Table 1. State transitions and rates for the single patch CTMC, Xt

Figure 1. The size of the susceptible population at the disease-free quasistationary distribution is given by β/μ. β is the independent variable, while μ=1,α=3.3,δ=1.3,ω=4 are fixed. The probability of extinction is approximated using branching process approximation (blue), numerical simulation via Gillespie algorithm (red) and LATS (black).

Figure 1. The size of the susceptible population at the disease-free quasistationary distribution is given by β/μ. β is the independent variable, while μ=1,α=3.3,δ=1.3,ω=4 are fixed. The probability of extinction is approximated using branching process approximation (blue), numerical simulation via Gillespie algorithm (red) and LATS (black).

Table 2. This table illustrates the convergence of entries in the row associated with x0 of QNN(x0) to the hitting probabilities h(x0;X)(E) and h(x0;X)(O).

Table 3. This table illustrates the convergence of entries in the row associated with x0 of H=(ID)1C to the hitting probabilities h(x0;X)(E) and h(x0;X)(O).

Table 4. Patch-specific and overall reproduction numbers for no control, control deployed in patch 1 only and control deployed in patch 2 only.

Table 5. State transitions and rates for the two-patch SIS CTMC, Xt

Table 6. Patch i reproduction numbers (R0i) and GWbp probabilities of extinction (1/R0i) and LATS probabilities of extinction (P0i) from the initial state (9,1) in patch i=1 and (4,1) in patch i=2 with and without control.

Table 7. Probability of partial extinction in patch 1 only and total extinction from initial state x0=(10,1,1,3).

Table 8. The probability of extinction from initial state x1=(7,1,7,0) and x2=(7,0,7,1).