Infectious diseases propagate along networks of contacts of infected hosts. Increasingly, epidemiological investigations have used molecular analysis and case investigation to reconstruct these infection paths, which are then quantified as “transmission trees”. Findings from such studies have shown that features like contact structure, heterogeneity, and the presence of “super spreaders” may be crucial to the propagation and containment of epidemics. Presently, such research is primarily case-based and there is no global understanding of the ubiquity of such features across epidemics more generally. The goal of this project is to develop the first comprehensive data base of transmission trees. The student will compile data from the published literature into a common format. These data will be analyzed to look for patterns in transmission that may be generalized to other epidemics. The work will be performed using the scientific programming language R.
Host Lab: John Drake
Project type: Quantitative/Computer-based