Table 2.

Descriptions of the parameters used in the proposed work.

Notation	Description
$L$	Spatial lattice
$A$	Set of possible states on lattice
$A\setminus 0$	Set of epidemiological states
$n_i^t$	Total number of people at state $a_i$ at time $t$
${\Omega }_d$	$d$-neighbourhood of $\mathbf{x}\in L$
${\Lambda }_t^a$	Mapping $L\to a$ at time $t$
$p_{ij}^t$	Probability at time $t$ that $\mathbf{x}\in L$ moves from $a_i$ to $a_j$
${\tau }_{ij}$	Transitional delay for $\mathbf{x}$ to move from $a_i$ to $a_j$
$e_t$, $i_t$	Number of exposed and infected people in the $d$-neighbourhood of $\mathbf{x}$ at time $t$
$p_e$, $p_i$	Probabilities that an exposed or an infected person spreads the infection to a susceptible person when they meet
$\Theta$	A gene containing all the parameters of PCA method
$B$	Binary encoded representation of $\Theta$
$G\left(\Theta \right)$	The PCA model with parameter $\Theta$
$\mathbf{y}$	Time series of an epidemiological state in a country
$\hat{\mathbf{y}}$	Time series estimate of epidemiological state from PCA
$e_{ji}$	Estimation error of $j$th gene in $i$th generation
$N_g$	Total number of chromosome in genepool
$F$	Number of parents selected for mating from $N_g$
$p_{\beta }$	Fraction of $r_t$ that recovers from the disease
$\rho$	Fraction of parents F that lives in the next generation