Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

Location map of the Pará State, Brazil.

More »

Fig 1 Expand

Fig 2.

Cases, deaths, and hospital beds occupancy caused by COVID-19 in the Pará State, Brazil.

More »

Fig 2 Expand

Fig 3.

ANN architecture with 1 neuron at input layer, 5 neurons and hyperbolic tangent activation function at hidden layer, and 2 neurons and linear activation function at output layer.

xj is the standardized or binary scale output of the j-th neuron of input layer when quantitative or categorical variables were used, respectively. w1ij is the synaptic weight that connects the output of the j-th neuron of the input layer to the input of the i-th neuron of the hidden layer. u1i is the result of the scalar product between xj and w1ij. b1i is the bias added to the i-th neuron of the hidden layer. y1i is the output of the i-th neuron from the hidden layer. w2ij is the synaptic weight that connects the output of the j-th neuron of the hidden layer to the input of the i-th neuron of the output layer. b2i is the bias added to the i-th neuron of the output layer. y2i is the output of the i-th neuron from the output layer.

More »

Fig 3 Expand

Table 1.

Prediction and trend measures of the best ANN for the test dataset.

More »

Table 1 Expand

Fig 4.

Forecasting of cumulative cases in the six analyzed scenarios.

More »

Fig 4 Expand

Fig 5.

Forecasting of cumulative deaths in the six analyzed scenarios.

More »

Fig 5 Expand

Fig 6.

Forecasting of daily cases for the six analyzed scenarios.

More »

Fig 6 Expand

Fig 7.

Forecasting of daily deaths in the six analyzed scenarios.

More »

Fig 7 Expand

Fig 8.

Forecasting of standard hospital beds occupancy in the six analyzed scenarios.

More »

Fig 8 Expand

Fig 9.

Forecasting of ICU beds occupancy in the six analyzed scenarios.

More »

Fig 9 Expand

Table 2.

Prediction and trend measures used to validate the forecasts.

More »

Table 2 Expand

Fig 10.

Percentual residuals for cumulative cases of the 6 analyzed scenarios.

More »

Fig 10 Expand

Fig 11.

Percentual residuals for cumulative deaths of the 6 analyzed scenarios.

More »

Fig 11 Expand

Fig 12.

Percentual residuals for daily cases of the 6 analyzed scenarios.

More »

Fig 12 Expand

Fig 13.

Percentual residuals for daily deaths of the 6 analyzed scenarios.

More »

Fig 13 Expand

Fig 14.

Percentual residuals for standard hospital beds occupancy of the 6 analyzed scenarios.

More »

Fig 14 Expand

Fig 15.

Percentual residuals for ICU bed occupancy in the 6 analyzed scenarios.

More »

Fig 15 Expand