Final Report Summary - BIG_IDEA (Building an Integrated Genetic Infectious Disease Epidemiology Approach) During this project, we developed novel computational methods taking advantage of genomic sequence data to reconstruct transmission chains (i.e. who infected whom) in disease outbreaks and epidemics. These methods were applied to several different human and wildlife pathogens and allowed to answer a series of questions. One of the most successful application was on tuberculosis (TB). The TB epidemic is fueled by a parallel Human Immunodeficiency Virus (HIV) epidemic, but it remained an open question to what extent the HIV epidemic has been a driver for drug resistance in Mycobacterium tuberculosis (Mtb). Our methods allowed us to assess the impact of HIV co-infection on the emergence of resistance and transmission of Mtb in the largest outbreak of multidrug-resistant TB in South America. By combining Bayesian evolutionary analyses and the reconstruction of transmission networks utilizing a new model optimized for TB, we found that HIV co-infection does not significantly affect the transmissibility or the mutation rate of Mtb within patients and was not associated with increased emergence of resistance within patients. Our results indicate that the HIV epidemic serves as an amplifier of TB outbreaks by providing a reservoir of susceptible hosts, but that HIV co-infection is not a direct driver for the emergence and transmission of resistant strains. This work demonstrates the power of genomic sequence data combined to sophisticated computational inference tools to address important questions in medicine and public health. The results should also alleviate stigma against HIV positive people and help optimising public health intervention to stem the rise of drug resistance in TB.
