The COVID-19 pandemic has demonstrated that health care capacity can be overloaded even in highly developed countries, resulting not only in inadequate care for COVID-19 patients but also in impacts on overall health system efficiency, such as reduced prevention and screening programs and delayed surgeries. The PROGNOSIS consortium brings together partners from five institutions with complementary expertise in biostatistics, bioinformatics, epidemiology, health services research, infectious diseases, and economics to address this pressing problem with a principled and holistic approach. Data-driven short-term and mechanistic long-term predictive models of hospital burden at different levels of care (standard, intensive, ventilator, extracorporeal membrane oxygenation / ECMO) for three important respiratory infections: COVID-19, influenza, and pneumococcal pneumonia are developed. Different episodes of the epidemic and different geographic levels (Germany, states, counties) are also considered to allow local predictions. The models are parameterized using extensive and continuously growing data sets from ongoing collaborations with several German institutions and competence networks. Based on this, the impact on hospital supply chains and human resources will also be modeled to derive specific short-term countermeasures to address the expected burden on hospitals and to assess the effectiveness of long-term measures such as vaccination programs and non-pharmaceutical interventions. The modeling approach is designed to be transferable to other pan- and epidemic situations.