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SI for From Serological Surveys to Disease Burden: A Modelling Pipeline for Chagas Disease

Additional file 2. Text S1. Additional text on quality assessment. Table S1. Boolean algorithms for literature search as of 5th of April 2018. Table S2. Pooled prevalence estimates of T. cruzi infection in both triatomines and potential animal reservoirs by departments (firs administrative units) in Colombia. Table S3. Detailed data for the 18 studies chosen for potential T. cruzi reservoirs in Colombia. Table S4. Detailed data for the 28 studies chosen for Trypanosoma cruzi triatomine vectors in Colombia. Table S5. Results of point or pooled prevalence estimates (meta-analysis using random effects model) by detailed diagnostic methods for the different orders of potential Trypanosoma cruzi animal reservoirs studied in Colombia. Table S6. Results of point or pooled prevalence estimates (meta-analysis using random effects model) of T. cruzi infection by detailed diagnostic methods for the different orders of triatomines studied in Colombia. Table S7. Results of Eggerʼs test for publication bias assessment for estimates of T. cruzi infection rates in animal reservoirs and triatomine vectors in Colombia. Table S8. Results of point or pooled prevalence estimates (meta-analysis using random effects model) for the different orders of potential Trypanosoma cruzi reservoirs studied in Colombia, using only high-quality reports. Table S9. Results of point or pooled prevalence estimates (meta-analysis using random effects model) of T. cruzi infection for the different orders of triatomines studied in Colombia, using only high-quality reports. Figure S1. Content quality assessment of the 39 studies used in the meta-analysis. Figure S2. Funnel plot of the 95% CI pseudo limits for the estimates of T. cruzi prevalence in animal reservoirs in Colombia.

Additional file 2. Text S1. Additional text on quality assessment. Table S1. Boolean algorithms for literature search as of 5th of April 2018. Table S2. Pooled prevalence estimates of T. cruzi infection in both triatomines and potential animal reservoirs by departments (firs administrative units) in Colombia. Table S3. Detailed data for the 18 studies chosen for potential T. cruzi reservoirs in Colombia. Table S4. Detailed data for the 28 studies chosen for Trypanosoma cruzi triatomine vectors in Colombia. Table S5. Results of point or pooled prevalence estimates (meta-analysis using random effects model) by detailed diagnostic methods for the different orders of potential Trypanosoma cruzi animal reservoirs studied in Colombia. Table S6. Results of point or pooled prevalence estimates (meta-analysis using random effects model) of T. cruzi infection by detailed diagnostic methods for the different orders of triatomines studied in Colombia. Table S7. Results of Eggerʼs test for publication bias assessment for estimates of T. cruzi infection rates in animal reservoirs and triatomine vectors in Colombia. Table S8. Results of point or pooled prevalence estimates (meta-analysis using random effects model) for the different orders of potential Trypanosoma cruzi reservoirs studied in Colombia, using only high-quality reports. Table S9. Results of point or pooled prevalence estimates (meta-analysis using random effects model) of T. cruzi infection for the different orders of triatomines studied in Colombia, using only high-quality reports. Figure S1. Content quality assessment of the 39 studies used in the meta-analysis. Figure S2. Funnel plot of the 95% CI pseudo limits for the estimates of T. cruzi prevalence in animal reservoirs in Colombia.