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Table 1 Summary table of the model fitting and estimation results. The models results summarized here are also estimated by using the full epidemic dataset during the whole epidemic period. The models with the lowest AICs (for the same states) are considered as main results, which matches the results in Fig 2. The numbers in parentheses are the 95% CIs

From: Simple framework for real-time forecast in a data-limited situation: the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example

State Population Model Durationa Epidemic notice period Final size Reproduction number Turning pointb Turning date R 2 AIC
Acre 803,513 Richards 148 26.11.2015–23.04.2016 783
(774–793)
2.13 (2.07–2.19) 111 (110–113) 17.03.2016
(15.03.2016–18.03.2016)
0.9995 108.1
Acre 803,513 Gompertz 148 26.11.2015–23.04.2016 925
(804–1045)
2.25 (1.82–2.76) 96 (91–100) 01.03.2016
(25.02.2016–05.03.2016)
0.9887 176.8
Acre 803,513 Logistic 148 26.11.2015–23.04.2016 858
(804–912)
3.45 (2.94–4.04) 101 (98–104) 07.03.2016
(03.03.2016–10.03.2016)
0.9934 162.9
Bahia 15,203,934 Richards 218 29.10.2015–04.06.2016 55,472
(54,683–56,260)
1.63 (1.58–1.68) 135 (132–139) 13.03.2016
(09.03.2016–16.03.2016)
0.9984 164.5
Bahia 15,203,934 Gompertz 218 29.10.2015–04.06.2016 59,773
(56,293–63,252)
1.89 (1.68–2.13) 113 (110–117) 20.02.2016
(16.02.2016–24.02.2016)
0.9885 227.8
Bahia 15,203,934 Logistic 218 29.10.2015–04.06.2016 58,896
(56,603–61,189)
2.21 (2.03–2.40) 121 (118–125) 27.02.2016
(24.02.2016–02.03.2016)
0.9922 213.5
Espirito Santo 3,929,911 Richards 84 23.01.2016–16.04.2016 2066
(1913–2219)
2.42 (1.78–3.25) 38 (32–44) 29.02.2016
(23.02.2016–07.03.2016)
0.9963 8.6
Espirito Santo 3,929,911 Gompertz 84 23.01.2016–16.04.2016 2293
(1993–2592)
2.11 (1.68–2.64) 30 (25–35) 22.02.2016
(16.02.2016–27.02.2016)
0.9940 14.8
Espirito Santo 3,929,911 Logistic 84 23.01.2016–16.04.2016 2132
(2020–2243)
3.05 (2.73–3.41) 35 (32–38) 27.02.2016
(24.02.2016–29.02.2016)
0.9960 7.6
Goiania City 6,610,681 Richards 155 10.12.2015–14.05.2016 na na na na na na
Goiania City 6,610,681 Gompertz 155 10.12.2015–14.05.2016 2578
(2437–2718)
1.89 (1.79–2.00) 104 (102–106) 23.03.2016
(21.03.2016–25.03.2016)
0.9989 192.5
Goiania City 6,610,681 Logistic 155 10.12.2015–14.05.2016 2243
(2184–2303)
3.07 (2.92–3.24) 109 (108–111) 29.03.2016
(27.03.2016–30.03.2016)
0.9991 186.2
Mato Grosso 3,265,486 Richards 134 29.10.2015–12.03.2016 na na na na na na
Mato Grosso 3,265,486 Gompertz 134 29.10.2015–12.03.2016 19,791
(18,147–21,435)
1.67 (1.56–1.79) 73 (69–76) 10.01.2016
(06.01.2016–14.01.2016)
0.9978 83.2
Mato Grosso 3,265,486 Logistic 134 29.10.2015–12.03.2016 17,165
(16,411–17,920)
2.47 (2.31–2.65) 79 (77–82) 17.01.2016
(14.01.2016–19.01.2016)
0.9974 84.2
Parana 11,163,018 Richards 141 14.01.2016–04.06.2016 na na na na na na
Parana 11,163,018 Gompertz 141 14.01.2016–04.06.2016 4382
(4162–4602)
1.92 (1.80–2.04) 79 (77–81) 02.04.2016
(31.03.2016–02.04.2016)
0.9984 102.1
Parana 11,163,018 Logistic 141 14.01.2016–04.06.2016 4008
(3894–4123)
2.82 (2.66–2.99) 86 (84–87) 09.04.2016
(07.04.2016–11.04.2016)
0.9986 98.4
Pernambuco 9,345,173 Richards 169 03.12.2015–21.05.2016 9936
(9770–10,102)
1.85 (1.75–1.95) 90 (87–93) 02.03.2016
(29.02.2016–05.03.2016)
0.9990 116.3
Pernambuco 9,345,173 Gompertz 169 03.12.2015–21.05.2016 10,721
(10,168–11,273)
1.85 (1.69–2.01) 76 (73–79) 17.02.2016
(14.02.2016–21.02.2016)
0.9945 158.4
Pernambuco 9,345,173 Logistic 169 03.12.2015–21.05.2016 10,323
(10,070–10,576)
2.33 (2.21–2.45) 84 (82–86) 25.02.2016
(23.02.2016–27.02.2016)
0.9975 136.8
Rio Grande 11,247,972 Richards 162 24.12.2015–04.06.2016 595
(492–698)
1.98 (1.65–2.36) 124 (119–129) 26.04.2016
(21.04.2016–01.05.2016)
0.9962 120.6
Rio Grande 11,247,972 Gompertz 162 24.12.2015–04.06.2016 772
(653–890)
1.54 (1.43–1.65) 120 (112–128) 22.04.2016
(14.04.2016–30.04.2016)
0.9971 114.5
Rio Grande 11,247,972 Logistic 162 24.12.2015–04.06.2016 634
(575–693)
2.15 (2.00–2.32) 124 (118–129) 26.04.2016
(20.04.2016–02.05.2016)
0.9960 119.4
  1. aThe “duration” is the epidemic reporting duration (in days) since the starting time (date; day.month.year) of the reported outbreak, which is the difference of the end and start dates of the “epidemic reporting period”
  2. bThe “turning point” is the estimated time period (in days) from the starting time (date; day.month.year) of the outbreak to the estimated occurrence of the turning point
  3. Abbreviations: AIC, Akaike information criterion; na, not applicable; this occurs when the fitting progress fails to converge for a few model frameworks; CI, confidence interval