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Table 2 Alternative ARIMAX models for the three cities

From: Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

City

Model

Normalized BIC value

P*

MAPE (%)a

Xuzhou

ARIMA (1,1,1)(0,1,1)12

8.857

0.861

12.54

ARIMA (1,1,1)(0,1,1)12 + MAS2

8.595

0.714

14.05

ARIMA (1,1,1)(0,1,1)12 + MAH1

8.467

0.399

24.09

ARIMA (1,1,1)(0,1,1)12 + MP2

8.617

0.356

11.96

ARIMA (1,1,1)(0,1,1)12 + MST1

8.593

0.767

17.62

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MAH1

8.609

0.338

25.02

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MP2

8.658

0.691

17.22

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MST1

8.679

0.902

17.34

ARIMA (1,1,1)(0,1,1)12 + MAH1 + MP2

8.560

0.431

20.68

ARIMA (1,1,1)(0,1,1)12 + MAH1 + MST1

8.604

0.416

24.30

ARIMA (1,1,1)(0,1,1)12 + MP2 + MST1

8.674

0.751

17.55

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MAH1 + MP2

8.700

0.371

20.71

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MAH1 + MST1

8.755

0.427

23.01

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MP2 + MST1

8.755

0.851

17.21

ARIMA (1,1,1)(0,1,1)12 + MAH1 + MP2 + MST1

8.692

0.241

39.17

ARIMA (1,1,1)(0,1,1)12 + MAS2 + MAH1 + MP2 + MST1

8.831

0.581

17.44

Nantong

ARIMA (0,1,1)(0,1,1)12

8.609

0.433

15.57

ARIMA (0,1,1)(0,1,1)12 + MAT0

8.288

0.981

16.77

ARIMA (0,1,1)(0,1,1)12 + MAP1

8.183

0.777

11.16

ARIMA (0,1,1)(0,1,1)12 + MAS2

8.323

0.730

16.29

ARIMA (0,1,1)(0,1,1)12 + MAT0 + MAP1

8.340

0.836

14.99

ARIMA (0,1,1)(0,1,1)12 + MAT0 + MAS2

8.419

0.965

16.97

ARIMA (0,1,1)(0,1,1)12 + MAP1 + MAS2

8.314

0.766

11.90

ARIMA (0,1,1)(0,1,1)12 + MAT0 + MAP1 + MAS2

8.470

0.892

13.06

Wuxi

ARIMA (0,1,1)(0,1,1)12

6.933

0.176

9.70

ARIMA (0,1,1)(0,1,1)12 + MAH0

6.845

0.119

9.66

ARIMA (0,1,1)(0,1,1)12 + MST0

6.818

0.068

10.51

ARIMA (0,1,1)(0,1,1)12 + MAH0 + MST0

7.003

0.088

9.74

  1. BIC Bayesian information criterion, MAPE mean absolute percentage error, MAT monthly average temperature; MAP monthly average atmospheric pressure, MAS monthly average wind speed, MAH monthly average relative humidity, MP Monthly precipitation, MST monthly sunshine time, 0 0-month lag, 1 1-month lag, 2 2-month lag
  2. *Ljung-Box test
  3. a MAPE of the model in predicting the monthly number of PTB cases in 2018
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Infectious Diseases of Poverty

ISSN: 2049-9957

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