Figure 1. Age-specific prevalence of dengue immunoglobulin G (IgG) in serological samples collected in Singapore in ... Figure 1. Age-specific prevalence.

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Figure 1. Age-specific prevalence of dengue immunoglobulin G (IgG) in serological samples collected in Singapore in ... Figure 1. Age-specific prevalence of dengue immunoglobulin G (IgG) in serological samples collected in Singapore in 2013 (A) and 2017 (B). The presence of dengue IgG in the serum samples was determined using the Panbio Dengue IgG Indirect ELISA (Alere Inc., Waltham, Massachusetts). Confidence intervals (black bars) were constructed using Wald's method. Participants aged 66 years or more were combined into one group because of the small sample size. ELISA, enzyme-linked immunosorbent assay. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2. Estimated annual dengue force of infection (FOI) in Singapore during the periods 1931–2017 (A) and 1990–2015 ... Figure 2. Estimated annual dengue force of infection (FOI) in Singapore during the periods 1931–2017 (A) and 1990–2015 (B). An overall declining trend in FOI estimates was observed from 1931 to 2017 (A); however, the FOI estimates increased slightly from 1999 to 2008 and then decreased (B). Solid lines represent the point estimates, and shaded regions represent the 95% Bayesian credible intervals. The model was developed using 2004, 2009, 2013, and 2017 serosurvey data. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3. Comparison of empirical and reconstructed prevalences of dengue immunoglobulin G from independent serosurveys ... Figure 3. Comparison of empirical and reconstructed prevalences of dengue immunoglobulin G from independent serosurveys carried out in Singapore in 2004, 2009, 2013, and 2017. Empirical estimates are shown as black dots and 95% Clopper-Pearson confidence intervals by black bars. Solid gray lines represent the model estimates for 2004 (A), 2009 (B), 2013 (C), and 2017 (D), with the gray shaded regions representing 95% Bayesian credible intervals. Degradation of the sensitivity of the enzyme-linked immunosorbent assay over time was taken into account in the reconstructed estimates. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 4. Dengue seroprevalence by age and year among residents of Singapore, generated with Bayesian modeling, 2009, ... Figure 4. Dengue seroprevalence by age and year among residents of Singapore, generated with Bayesian modeling, 2009, 2013, and 2017. Lines represent posterior median values for seroprevalence, and shaded areas represent 95% Bayesian credible intervals. A) Seroprevalence in the years 2009, 2013, and 2017 based on age in 2009. Within each birth cohort, only a marginal increase in seroprevalence is observable from 2009 to 2013 and from 2013 to 2017. The gap is most prominent in the youngest age group. B) Seroprevalence in the years 2009, 2013, and 2017 based on age in 2009, 2013, and 2017, respectively. Holding age fixed (in 2009, 2013, and 2017), the estimated prevalence for persons over age 23 years was lower in 2013 than in 2009 and lower in 2017 than in 2013. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 5. Estimated annual basic reproduction number (R0) for dengue (A) and reconstructed annual dengue ... Figure 5. Estimated annual basic reproduction number (R<sub>0</sub>) for dengue (A) and reconstructed annual dengue seroprevalence (B) in Singapore, 1960–2020. Solid lines represent the point estimates, and shaded regions represent 95% Bayesian credible intervals. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 6. Estimated age-specific dengue seroprevalence in Singapore in 2017, modeled from 4 hypothetical circumstances: ... Figure 6. Estimated age-specific dengue seroprevalence in Singapore in 2017, modeled from 4 hypothetical circumstances: force of infection (FOI) fixed at the 1960s, 1970s, 1980s, and 1990s averages, respectively. The black line represents the age-specific seroprevalence in 2017 based on the original FOI estimates. Of the 4 hypothetical situations, the largest number of dengue infections averted occurs when the FOI is held fixed at the 1960s average (3.12 million; 95% Bayesian credible interval (BCrI): 2.94, 3.27), assuming that numbers of primary and subsequent dengue infections are similar. For scenarios where FOI is held fixed at the 1970s, 1980s, and 1990s averages, the numbers of dengue infections averted are estimated to be 1.90 million (95% BCrI: 1.63, 2.14), 0.87 million (95% BCrI: 0.61, 1.13), and −0.11 million (95% BCrI: −0.30, 0.07), respectively. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journalpermissions@oup.com. American Journal of Epidemiology, Volume 188, Issue 8, 07 May 2019, Pages 1529–1538, https://doi.org/10.1093/aje/kwz110 The content of this slide may be subject to copyright: please see the slide notes for details.