]>Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts : Table 4
Table 4: Seasonal outbreak data involving a parabolic change in means (a is the multiplicative adjustment needed from the theoretical threshold).
Model number
1 2 3 1 2 3
Dynamic Poisson adaptive EWMA Poisson adaptive EWMA
𝜃 in (3.3)0.0750.10.1
𝑎 1.00761.00751.00761.00561.00551.0050
( 𝐿 , 𝑗 Δ ) 𝑗 = 0 . 5 𝑗 = 0 . 5 𝑗 = 1 . 0 𝑗 = 0 . 5 𝑗 = 0 . 5 𝑗 = 1 . 0
Overall average number of extra victims in the outbreak data
∑ 𝑒 𝑡 = 1 𝐿 𝛿 𝑒 𝑡 424284424284
ATS (Pr(no outbreak data signal))
(0,0) 102.38 (0.00)100.96 (0.00)97.23 (0.00)99.50 (0.00)100.19 (0.00)103.03 (0.00)
( 4 3 , 𝑗 × 0 . 0 0 3 1 7 ) 22.05 (0.16)14.73 (0.03)16.11 (0.07) 18.56 (0.14) 15.31 (0.03)17.00 (0.07)
( 3 6 , 𝑗 × 0 . 0 0 5 4 0 5 5 ) 18.93 (0.14)12.36 (0.02)13.57 (0.06) 15.72 (0.14)12.59 (0.02)14.17 (0.06)
( 2 9 , 𝑗 × 0 . 0 1 0 3 5 ) 14.62 (0.12)9.66 (0.01)10.88 (0.03)12.85 (0.13)10.37 (0.01)11.61 (0.03)
( 2 2 , 𝑗 × 0 . 0 2 3 7 1 5 ) 11.20 (0.11)7.54 (0.00)8.43 (0.03) 9.81 (0.11)7.60 (0.00)8.65 (0.03)
( 1 5 , 𝑗 × 0 . 0 7 5 ) 7.44 (0.10)4.75 (0.00)5.03 (0.02) 6.66 (0.08)4.93 (0.00)5.69 (0.02)
( 8 , 𝑗 × 0 . 5 ) 3.50 (0.04)2.40 (0.00)2.81 (0.00)3.33 (0.05)2.43 (0.00)2.83 (0.00)