Integrating respiratory infection surveillance and temperature data improves all-season mortality reconstruction

All-season mortality surveillance can inform public-health planning under climate change, but attribution is complicated by overlapping effects of temperature, respiratory infections and other non-environmental factors. Here we develop a component-based neural-network model for daily all-cause mortality that combines high-resolution temperature and dew-point data, severe acute respiratory infection (SARI) hospitalization incidence, demographic structure and an adaptive mortality baseline. In Germany, inputs included non-COVID SARI and COVID-19-associated SARI. Using 2014–2025 mortality data, the hybrid model reduced daily root-mean-square error to 80.2 deaths, compared with 170.2 for a weather-only model and 117.1 for an infection-only model, and enabled age- and sex-resolved examination of fitted components. Including respiratory-infection indicators reduced the increase in risk assigned by the model to cold exposure, suggesting that temperature- only models may partly assign winter infection-related variation to cold. Component magnitudes represent conditional model decompositions rather than causal estimates of deaths due to SARI or temperature.