Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health

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.

Exploring HIV-1 Transmission Dynamics by Combining Phylogenetic Analysis and Infection Timing

“…The goal of this initiative is to identify not yet diagnosed infections through partner tracing of the newly diagnosed individuals. The project has been successfully implemented in Ukraine, Greece, and the US [11,[48][49][50]. The combination of partner tracing initiatives with real-time phylogenetics and infection time analysis may increase the effectiveness of the approach because it will allow to focus on the most active transmission clusters.…”

Molecular epidemiology of a primarily MSM acute HIV‐1 cohort in Bangkok, Thailand and connections within networks of transmission in Asia

“…The envelope glycoprotein (encoded by env ) mediates viral entry into host cells, and though it is partially shielded by glycans from the immune response, it possesses the highest genetic diversity and evolutionary rate of any gene in the HIV‐1 genome . Understanding env ‐based networks may yield insight into viral pathogenesis, for a strain that has a high degree of connectivity may possess some advantage with respect to infectivity or fitness . Translated to approaches for vaccine design in which the HIV‐1 envelope glycoprotein is a target, trends in env ‐based transmission networks could provide more contemporaneous data to show that current vaccine candidates may still cover the majority of circulating subtypes despite increasing diversity and recombination, and could also inform selection for vaccines in the pipeline.…”

Goat movement network analysis and its implications for caprine brucellosis propagation in Nonthaburi Province, Thailand

“…The movement of animals with different sources and destinations is represented as a network. Network analysis, therefore, is a useful tool for estimating the risk related to disease transmission [9]. In Thailand, animal movement network analysis has been applied to assess the potential for infectious disease diffusion in backyard chickens [10,11] and cattle [12].…”

“…Network analysis has been used in a variety of settings such as infectious disease surveillance system, disease transmission pathway or even prediction of further epidemics and planning preventive interventions [9]. For example, the technique was applied to explore a traditional cattle trade network in Thailand and to address diseases spread probability [12].…”