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Table 2 Multivariate seasonal autoregressive integrated moving average (SARIMA) models of malaria incidence in four administrative areas in Swaziland

From: Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination

SARIMA modela Coefficients SE AIC AIC difference
Hhohho
 Malaria only    9.6
 Malaria + MEI (lag = 2) −0.067 0.049 11.67 2.07
 Malaria + TMAX (lag = 0) 0.0137 0.0152 10.97 1.37
 Malaria + TMIN (lag = 3) 0.0124 0.0089 12.58 2.98
 Malaria + precipitation (lag = 3) 0.02 0.0066 5.43 b 4.17
Lubombo
 Malaria only    92.34
 Malaria + MEI (lag = 1) −0.2039 0.05 89.46 −2.88
 Malaria + TMAX (lag = 3) 0.0449 0.0775 88.88 −3.46
 Malaria + TMIN (lag = 1) 0.0135 0.0092 92.22 −0.12
 Malaria + precipitation (lag = 2) 0.0224 0.0007 86.59 b 5.75
Manzini
 Malaria only   474.99 b
 Malaria + MEI (lag = 3) 0.0054 0.0085 −471.65 3.34
 Malaria + TMAX (lag = 3) 0.7475 0.3119 −471.83 3.16
 Malaria + TMIN (lag = 2) 0.0004 0.0024 −471.27 3.72
 Malaria + precipitation (lag = 1) 0.0054 0.0025 −471.12 3.87
Shiselweni
 Malaria only   396.8 b
 Malaria + MEI (lag = 7) 0.0156 0.0139 −375.52 21.28
 Malaria + TMAX (lag = 4) 0.009 0.004 −389.54 7.26
 Malaria + TMIN (lag = 2) 0.0039 0.0023 −393.72 3.08
 Malaria + precipitation (lag = 3) 0.006 0.0032 −390.88 5.92
  1. aThe lag is selected using a cross-correlation function
  2. bThe model with the lowest AIC value is indicated in italic type