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Additional file 1: Table S1. Diagnostic tests to confirm rickettsia infection and to exclude S. typhi, Dengue, Leptospira, and Chikungunya infections and diagnostic tests to confirm rickettsia infection in an HIV patient and 31 patients with non-rickettsial clinical diagnoses.

Table S1. Number of influenza cases caused by the difference influenza viruses that were included in the analysis. The Global Influenza B Study, 1999-2014. (DOCX 24 kb)

Table S2. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by categories of country ageing index. The Global Influenza B Study, 1999-2014. Table S3. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of outpatients among cases reported to the influenza surveillance system. The Global Influenza B Study, 1999-2014. Table S4. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by country latitude. The Global Influenza B Study, 1999-2014. Table S5. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of influenza cases caused by that influenza virus in the same season. The Global Influenza B Study, 1999-2014. Table S6. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of influenza cases caused by that influenza virus in the previous season. The Global Influenza B Study, 1999-2014. Table S7. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by categories of country gross domestic product (GDP) per capita. The Global Influenza B Study, 1999-2014. (DOCX 46 kb)

Background Longitudinal data are limited about the circulating strains of influenza viruses and their public health impact in Indonesia. We conducted influenza surveillance among outpatients and hospitalized patients with influenza‐like illness (ILI) across the Indonesian archipelago from 2003 through 2007. Methodology Demographic, clinical data, and respiratory specimens were collected for 4236 ILI patients tested for influenza virus infection by RT‐PCR and viral culture. Principal Findings Influenza A and B viruses co‐circulated year‐round with seasonal peaks in influenza A virus activity during the rainy season (December–January). During 2003–2007, influenza viruses were identified in 20·1% (4236/21 030) of ILI patients, including 20·1% (4015/20 012) of outpatients, and 21·7% (221/1018) of inpatients. One H5N1 case was identified retrospectively in an outpatient with ILI. Antigenic drift in circulating influenza A and B virus strains was detected during the surveillance period in Indonesia. In a few instances, antigenically drifted viruses similar to the World Health Organization (WHO) vaccine strains were detected earlier than the date of their designation by WHO. Conclusions Influenza A and B virus infections are an important cause of influenza‐like illness among outpatients and hospitalized patients in Indonesia. While year‐round circulation of influenza viruses occurs, prevention and control strategies should be focused upon the seasonal peak during rainy season months. Ongoing virologic surveillance and influenza disease burden studies in Indonesia are important priorities to better understand the public health impact of influenza in South‐East Asia and the implications of influenza viral evolution and global spread.

Background: Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases). Methods: For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted metaregression and sub-group analyses to explore causes of between-estimates heterogeneity. Results: The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1) pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I-2>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play. Conclusions: These results highlight the importance of presenting burden of disease estimates by age group and virus (sub) type.

Background: Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases). Methods: For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted metaregression and sub-group analyses to explore causes of between-estimates heterogeneity. Results: The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1) pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I-2>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play. Conclusions: These results highlight the importance of presenting burden of disease estimates by age group and virus (sub) type.