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Table 4 Forecasting performance for 1-month horizon across Brazilian states using different models

From: Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects

Federal Unit (FU)

Code

LSTM-Cases

LSTM-Climate

LSTM-Climate-Spatial

Bayesian Baseline

MAE

MAPE

CRPS

MAE

MAPE

CRPS

MAE

MAPE

CRPS

MAE

MAPE

CRPS

Acre (AC)

12

305.19

45.50%

90.91

129.76

22.30%

35.68

136.83

24.89%

37.34

382.77

47.23%

96.13

Alagoas (AL)

27

177.96

43.29%

38.14

79.24

30.54%

16.27

61.08

23.17%

12.98

69.39

24.28%

13.41

Amap谩 (AP)

16

51.21

47.90%

34.05

22.45

23.49%

5.35

27.45

26.98%

6.02

30.53

34.09%

7.12

Amazonas (AM)

13

188.17

41.56%

32.14

100.21

19.63%

19.23

111.60

21.64%

22.44

143.57

28.79%

31.40

Bahia (BA)

29

886.64

29.94%

165.30

639.44

23.20%

123.86

532.46

17.13%

120.50

718.63

22.84%

137.74

Cear谩 (CE)

23

562.67

46.52%

108.09

245.17

27.54%

52.99

187.56

15.51%

35.01

315.69

30.16%

60.26

Distrito Federal (DF)

53

1040.21

26.69%

244.60

926.73

23.24%

219.97

767.30

16.72%

211.70

997.42

24.57%

249.25

Esp铆rito Santo (ES)

32

8431.94

30.90%

1713.56

7262.14

30.35%

1310.95

6300.78

23.06%

1308.43

6967.74

25.93%

1552.96

Goi谩s (GO)

52

1708.00

30.34%

310.44

1277.24

27.36%

226.00

1195.70

19.87%

222.87

1722.08

29.75%

321.36

Maranh茫o (MA)

21

143.59

56.31%

26.44

102.87

38.07%

18.93

59.27

23.91%

10.88

147.31

53.05%

28.14

Mato Grosso (MT)

51

657.81

34.65%

189.42

563.40

26.97%

142.56

340.72

16.69%

72.73

624.21

28.36%

125.27

Mato Grosso do Sul (MS)

50

1711.05

75.23%

342.47

568.10

59.97%

108.71

404.48

40.11%

81.94

1646.17

50.03%

344.61

Minas Gerais (MG)

31

15099.46

52.28%

3253.80

7730.47

33.33%

1648.53

5088.71

24.52%

1035.86

14220.67

40.19%

3472.85

Par谩 (PA)

15

178.13

35.24%

39.22

98.64

23.21%

18.35

101.22

23.95%

20.38

174.33

35.36%

42.92

Para铆ba (PB)

25

190.60

29.09%

48.24

129.42

25.04%

31.52

116.80

17.72%

24.68

141.34

26.51%

29.61

Paran谩 (PR)

41

9083.89

78.92%

2147.66

6730.21

67.56%

941.83

4450.76

31.37%

606.80

8025.29

54.30%

1909.77

Pernambuco (PE)

26

180.75

24.52%

39.93

138.94

19.21%

32.38

116.29

15.84%

20.96

157.04

22.33%

35.10

Piau铆 (PI)

22

261.43

44.46%

116.97

154.99

33.94%

66.48

140.58

23.39%

30.21

143.47

29.98%

32.34

Rio de Janeiro (RJ)

33

1599.72

38.27%

332.21

737.28

27.17%

128.86

532.26

16.31%

122.86

1524.53

38.43%

271.95

Rio Grande do Norte (RN)

24

237.30

39.26%

48.96

151.58

19.37%

39.75

100.87

17.10%

18.27

214.70

28.15%

51.01

Rio Grande do Sul (RS)

43

1983.01

160.62%

505.22

979.15

78.99%

249.31

396.54

56.30%

80.54

1354.97

113.82%

326.95

Rond么nia (RO)

11

371.50

79.49%

103.44

300.93

42.08%

79.71

285.61

40.93%

69.17

323.33

43.03%

104.34

Roraima (RR)

14

9.33

40.63%

6.03

6.52

43.27%

5.02

6.36

44.31%

5.93

8.66

52.55%

2.55

Santa Catarina (SC)

42

7381.10

79.16%

1765.31

1585.71

56.28%

395.28

1556.58

15.21%

294.09

3028.00

40.68%

831.25

S茫o Paulo (SP)

35

9544.39

49.61%

2196.34

4088.67

31.97%

921.95

3068.46

17.28%

612.34

8468.53

31.64%

1961.50

Sergipe (SE)

28

54.35

20.82%

13.07

45.92

17.48%

9.43

41.52

16.50%

8.11

86.08

31.38%

18.57

Tocantins (TO)

17

124.25

49.07%

31.51

103.21

37.89%

28.46

92.55

29.12%

20.44

128.97

45.07%

27.20