Chronic carriers of major hepatitis viruses (i.e., hepatitis B and C viruses, HBV and HCV) account for at least 600 millions people worldwide. About 50% of them are at risk for chronic hepatitis and 20-30% of patients with chronic hepatitis develop progressive liver disease and symptomatic life-threatening liver lesions. Therefore, the identification of the carrier at risk is mandatory to prevent progressive liver disease, avoiding non-appropriate treatments. The decision making has three major steps. The 1st is the identification of the patient who needs to be treated; the 2nd is the choice of the best therapeutic strategy and the most appropriate drugs and timing during the phase of infection and disease; the 3rd is the treatment optimization to reduce non effective therapy and avoid drug resistance virus mutants. This careful evaluation takes into account the individual variability, the host/virus interplays and the drug impact on viral replication with the risk of selection of resistant mutants. The complexity of the virus/host interactions, however, cannot be managed by simple mean of probabilistic statistics and/or step-wise algorithms based on population statistics. A better answer for personalized antiviral therapy may come from the combined use of molecular biology and bio-mathematical modeling that can help the medical doctor to follow the dynamic of viral infection during therapy, like the flight simulator helps the pilot. We provide a concise review of the potentials of this approach in clinical practice.