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Table 2 SARIMAX model performance summary

From: Driving role of climatic and socioenvironmental factors on human brucellosis in China: machine-learning-based predictive analyses

Geographical region

City

\({\mathrm{SARIMAX}(\mathrm{1,1},1)(\mathrm{P},\mathrm{Q},\mathrm{D})}_{\mathrm{S}=12}\) results

exg

P

Q

D

AIC

BIC

HQIC

SE (exg)

Ljung-Box (LB)

Jarque–Bera (JB)

Heteroskedasticity (H)

Northeast China

Baicheng

MAP

1

2

0

424.793

434.044

428.274

0.1

0.25

5.1

0.54

MAS

1

2

0

424.398

433.649

427.879

0.069

0.18

5.55

0.48

MAH

1

2

0

424.896

434.146

428.377

0.448

0.23

6.2

0.53

MAWS

1

2

0

421.492

430.743

424.973

3.19

0.15

13.98

0.54

MAT

1

2

0

424.802

434.053

428.283

1.29

0.19

4.17

0.52

Central China

Datong

MAP

1

2

0

465.631

474.882

469.113

0.078

0.01*

8.46

0.5

MAS

1

2

0

470.636

479.886

474.117

0.109

0.13

12.28

0.39

MAH

1

2

0

468.942

478.193

472.423

0.376

0.23

13.37

0.47

MAWS

1

2

0

471.73

480.98

475.211

4.495

0.35

16.17

0.43

MAT

1

2

0

470.617

479.868

474.099

1.77

0.21

6.05

0.56

Western China

Jinchang

MAP

0

1

0

434.754

443.064

437.998

0.082

0.37

1.88

0.78

MAS

0

1

0

434.841

443.152

438.085

0.025*

0.42

1.81

0.77

MAH

0

1

0

434.49

442.801

437.734

0.112

0.38

2.07

0.65

MAWS

0

1

0

434.885

443.195

438.129

3.825

0.39

2

0.73

MAT

0

1

0

434.649

442.959

437.893

0.648

0.43

2.37

0.68

Eastern China

Zhangjiakou

MAP

0

1

0

723.344

731.654

726.587

0.169

0.01*

16.25

0.65

MAS

0

1

0

721.686

729.996

724.93

0.255

0.03*

59.88

1.09

MAH

0

1

0

716.653

724.964

719.897

0.477

0*

6.59

0.61

MAWS

0

1

0

717.534

725.844

720.778

7.768

0.08

4.66

0.81

MAT

0

1

0

723.143

731.453

726.387

0.81

0.03*

41.33

0.91

  1. *\(P < 0.05\)