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Purpose: Timeliness is the key feature of detection and response of emerging infectious diseases outbreaks and especially hemorrhagic fevers. To better understand gaps, underlining reasons and propose improvements of surveillance systems a multi-country analysis and evaluation of Time to Detect and Time to Respond (TDTR) to haemorrhagic fever outbreaks has been carried out by Southeast European Center for Surveillance and Control of Infectious Diseases (SECID).Methods & Materials: A standardized spreadsheet template has been developed and used by Albania, Kosovo* and Bulgaria to collect in a structured format the surveillance and response timelines data. The analysis of time lags between detection, confirmation, reporting and responding was estimated through descriptive methods based on measures of central tendency and accompanied by related standard deviations and complemented with graphical representations through bar charts and box plots.Results: The data and surveillance process analysis show that the existing surveillance activities present notable gaps. We measured three main days interval groups:

Background The 2013–2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported. Methods Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. Results The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5–75.6%); specificity, 69.8% (54.1–75.6%); percentage correctly classified, 69.9% (53.7–75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8–82.1%). The adjusted CFR estimates (95% CI) for the 2013–2016 West African epidemic were 82.8% (45.6–85.6%) overall and 89.1% (40.8–91.6%), 65.6% (61.3–69.6%), and 79.2% (45.4–84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR. Conclusions The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.