Abstract:

The number of public health applications for molecular epidemiology and social network analysis has increased rapidly since the improvement in computational capacities and the development of new sequencing techniques. Currently, molecular epidemiology methods are used in a variety of settings: from infectious disease surveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic developments, and plan preventive interventions. Social network methods are used to understand how infections spread through communities and what the risk factors for this are, as well as in improved contact tracing and message-dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully sufficient information on infection transmission pathways. The main differences between the two data sources are that, firstly, social network data include uninfected individuals unlike the molecular data sampled only from infected network members. Thus, social network data include more detailed picture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have been attempts to combine and compare the data obtained from the two sources. Even though there is no consensus on whether and how social and genetic data complement each other, this research might significantly improve our understanding of how viruses spread through communities.