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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