Public health is concerned with measures that improve the general health and prevent infections. In my talk, I will give an overview and outlook of our current work and explain how data science in conjunction with mathematical modeling and simulation can be utilized to guide public health decisions. In particular, I will present approaches that utilize primary and secondary data of SARS-CoV-2 to permanently monitor and assess the pandemic. Moreover, I will give examples where these approaches supported the choice of containment and testing strategies in 2020/21. I will then give some insight into our ongoing work in the field of HIV-1 prevention, the mathematical methods developed along the way, and illustrate how this work is used to quantify risk reduction, to develop guidelines, as well as to a posteriori assess the impact of interventions on the HIV pandemic.