Abstract
Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of ‘peripheral nodes’ that have only a few sexual interactions and a minority of ‘hub nodes’ that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks can have important repercussions in the design of intervention strategies for disease control.
Highlights
Understanding the dynamics of infectious disease spreading demands a holistic approach [1]
Scientists have been trying to study the spread of infectious diseases using social or sexual contact networks, modelling the population as a complex network and running models of disease spread on top of that
Of the 655 patients included in the analysis, 65.0% were males and 35.0% females; HIV transmission risk categories were 27.0% men who have sex with men’ (MSM), 39.0% heterosexual contacts, 33.0% injecting drug users (IDU), 1.0% infected through blood products; 84.4% were Italian-born, 10.4% non-Italian born, while for 5.2% nation of birth was unknown
Summary
Understanding the dynamics of infectious disease spreading demands a holistic approach [1]. In the case of type HIV-1 infection, these models have been used to understand the complexity of HIV-1 transmission and spread of viral drug resistance [2,3,4,5,6,7,8]. These models require estimation of many parameters such as frequency of sexual actions, transmission probability per action, and parameters that shape the network structure. The primary assumptions on the network structure and the choice of the uncertain parameter values to build a sexual contact network are still controversial
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