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Table 2 Summary table of the real-time estimation results from the selected models. The model with the lowest AIC (for the same states) is selected for analysis. The models results using the full epidemic dataset during the whole epidemic period match the models with the lowest AICs in Table 1. 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 Model Durationa Fitting period Final size Reproduction number Turning pointb Turning date
Acre Richards 120 26.11.2015–26.03.2016 908
(576–1239)
2.16 (2.05–2.27) 113
(107–118)
18.03.2016
(13.03.2016–23.03.2016)
Acre Richards 127 26.11.2015–02.04.2016 772
(747–797)
2.12 (2.05–2.18) 112
(110–114)
17.03.2016
(15.03.2016–19.03.2016)
Acre Richards 134 26.11.2015–09.04.2016 776
(761–790)
2.12 (2.06–2.18) 112
(110–114)
17.03.2016
(15.03.2016–19.03.2016)
Acre 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)
Bahia Richards 155 29.10.2015–02.04.2016 50,249
(46,852–53,646)
1.61 (1.57–1.65) 137
(131–143)
14.03.2016
(09.03.2016–20.03.2016)
Bahia Richards 162 29.10.2015–09.04.2016 51,709
(49,237–54,181)
1.61 (1.57–1.66) 137
(132–141)
14.03.2016
(09.03.2016–19.03.2016)
Bahia Richards 169 29.10.2015–16.04.2016 52,963
(50,927–55,000)
1.61 (1.57–1.66) 136
(132–141)
14.03.2016
(09.03.2016–18.03.2016)
Bahia Richards 218 29.10.2015–04.06.2016 55,472
(54,683–56,260)
1.63 (1.58–1.68) 135
(132–139)
14.03.2016
(09.03.2016–16.03.2016)
Espirito Santo Logistic 42 23.01.2016–05.03.2016 1671
(766–2577)
3.48 (2.22–5.30) 27
(9–46)
19.02.2016
(01.02.2016–08.03.2016)
Espirito Santo Logistic 49 23.01.2016–12.03.2016 2126
(1112–3140)
2.93 (2.15–3.96) 35
(17–54)
27.02.2016
(09.02.2016–16.03.2016)
Espirito Santo Logistic 56 23.01.2016–19.03.2016 2364
(1609–3119)
2.75 (2.21–3.40) 39
(26–53)
02.03.2016
(18.02.2016–15.03.2016)
Espirito Santo 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)
Goiania City Logistic 113 10.12.2015–02.04.2016 2040
(1751–2329)
3.33 (3.05–3.63) 106
(102–111)
25.03.2016
(21.03.2016–30.03.2016)
Goiania City Logistic 120 10.12.2015–09.04.2016 2230
(2015–2445)
3.19 (2.98–3.41) 109
(105–112)
28.03.2016
(25.03.2016–31.03.2016)
Goiania City Logistic 127 10.12.2015–16.04.2016 2092
(1974–2210)
3.32 (3.12–3.53) 107
(105–109)
26.03.2016
(24.03.2016–28.03.2016)
Goiania City 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)
Mato Grosso Gompertz 77 29.10.2015–14.01.2016 12,901
(8235–17,567)
2.03 (1.61–2.55) 58
(47–69)
26.12.2015
(16.12.2015–06.01.2016)
Mato Grosso Gompertz 85 29.10.2015–23.01.2016 15,093
(10,750–19,436)
1.85 (1.57–2.18) 63
(53–73)
31.12.2015
(22.12.2015–10.01.2016)
Mato Grosso Gompertz 92 29.10.2015–30.01.2016 17,550
(12,927–22,172)
1.72 (1.51–1.96) 68
(58–78)
05.01.2016
(26.12.2015–15.01.2016)
Mato Grosso 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)
Parana Logistic 92 14.01.2016–16.04.2016 4121
(2604–5637)
2.90 (2.41–3.47) 86
(73–99)
09.04.2016
(27.03.2016–22.04.2016)
Parana Logistic 99 14.01.2016–23.04.2016 3720
(3048–4393)
3.05 (2.63–3.53) 82
(75–89)
06.04.2016
(30.03.2016–13.04.2016)
Parana Logistic 106 14.01.2016–30.04.2016 3610
(3228–3992)
3.12 (2.76–3.51) 81
(77–86)
05.04.2016
(31.03.2016–09.04.2016)
Parana 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)
Pernambuco Richards 106 03.12.2015–19.03.2016 10,694
(4222–17,165)
1.81 (1.63–2.01) 94
(78–109)
06.03.2016
(19.02.2016–22.03.2016)
Pernambuco Richards 113 03.12.2015–26.03.2016 9480
(7737–11,224)
1.78 (1.67–1.89) 91
(88–95)
04.03.2016
(29.02.2016–07.03.2016)
Pernambuco Richards 120 03.12.2015–02.04.2016 9320
(8512–10,127)
1.77 (1.68–1.87) 91
(88–94)
03.03.2016
(29.02.2016–07.03.2016)
Pernambuco 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)
Rio Grande Gompertz 148 24.12.2015–21.05.2016 771
(563–979)
1.54 (1.39–1.71) 120
(107–134)
22.04.2016
(09.04.2016–06.05.2016)
Rio Grande Gompertz 155 24.12.2015–28.05.2016 765
(613–917)
1.54 (1.42–1.68) 120
(110–130)
22.04.2016
(12.04.2016–02.05.2016)
Rio Grande 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)
  1. aThe “duration” is the fitting duration (in days) since the starting time (date; day.month.year) for fitting, which is the difference of the end and start dates of the “fitting 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