An analysis of fractional piecewise derivative models of dengue transmission using deep neural network

Figures & data

Figure 1. Schematic diagram of the dengue model.

Table 1. Initial and parameters numerical values for dengue model.

Parameter	Value	Parameter	Value	Parameter	Value
${\mathcal{S}}_H$	42020	$b_H$	10	${\gamma }_M$	0.001
${\mathcal{E}}_H$	3310	$b_M$	3.09	${\eta }_H$	0.034
${\mathcal{I}}_H$	300	$d_H$	0.004	$p_1$	0.0012
${\mathcal{R}}_H$	0	$d_M$	0.07	$p_2$	0.05
${\mathcal{S}}_M$	2580	${\text{β}}_H$	0.0005	$p_3$	0.00011
${\mathcal{E}}_M$	1225	${\text{β}}_M$	0.0012	$r_H$	0.331
${\mathcal{I}}_M$	1000	${\gamma }_H$	0.0001	$r_M$	0.021

Figure 2. Graphically representation of class $S_H\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 3. Graphically representation of class $E_H\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 4. Graphically representation of class $I\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 5. Graphically representation of class $R\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 6. Graphically representation of class $S_m\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 7. Graphically representation of class $E_M\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.

Figure 8. Graphically representation of class $I_M\left(t\right)$ with deep neural network. (a) All data, (b) train data, (c) test data, (d) validation and (e) comparison of AB with NN.