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Background Seasonally-adjusted linear models ('Serfling'models) serve as an important surveillance measure to estimate influenza (flu) attributable deaths for resource allocation to public health programs (eg vaccination campaigns). We compared performance of traditional time-series and viral activity-based models to a novel open-source R-package'Prophet'for estimating the number of deaths attributable to influenza per season. Methods We evaluated deaths from the 122-Cities Mortality Reporting System which reports the total number of death certificates where pneumonia or flu was listed as a contributing cause of death. Models were fitted to 2010–2015 influenza seasons. The first Serfling model (M1) used baseline periods of low-endemicity (summer months) to estimate attributable deaths during a flu season (OCT-MAY), the second Serfling model (M2) incorporated both baseline and virology data (count …