Infections caused by Human Immunodeficiency virus type 1 (HIV-1) is an important cause of death globally. Even though antiretroviral therapy works well to suppress the infection and practically eliminate the risk of infecting others, there are no vaccines or cures for HIV-1 infections. Persons who are unaware of their HIV infection pose a greater risk of spreading the infection compared to those with known and treated infection. Preventive work is therefore important. Unfortunately, we rarely know when a person has become infected, we only know when they have been diagnosed. There are indications that as many as 50% of HIV-cases in Sweden are not discovered until years after time of infection. There are methods available to calculate HIV-1 incidence based on biomarkers such as CD4 cell count, BED-antibodies, and virus levels. We have improved upon these methods by developing a mathematical model where time of infection in HIV-patients is estimated by applying Whole Genome Sequencing (WGS) of HIV-1 and using the sequence data to measure intrapatient genetic diversity. We then combine this information with previously mentioned biomarkers to get a more accurate estimated time of infection (ETI) as compared to using individual biomarkers alone. The goal of this project is primarily to establish a method for estimating time of infection which can then be used for research purposes as well as in clinical epidemiology practice.