Recently, multiple methods have been developed to infer properties of transmission networks from molecular data ( Kosakovsky Pond et al., 2018 Prosperi et al., 2011 Ragonnet-Cronin et al., 2013). Some viruses, such as HIV, evolve quickly, and the phylogenetic relationships between viruses are reflective of transmission histories ( Leitner et al., 1996), albeit imperfectly ( Leitner and Romero-Severson, 2018 Romero-Severson et al., 2014 Ypma et al., 2013). If the pathogens of the infected individuals are sequenced, which is the standard of HIV care in many developed countries, one can attempt to reconstruct the transmission network (or its main features) using molecular data.
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For example, network-based statistics for measuring the effects of Antiretroviral Therapy (ART) in Human Immunodeficiency Virus (HIV) can yield increased statistical power ( Wertheim et al., 2011) the analysis of the growth of HIV infection clusters can yield actionable epidemiological information for disease control ( Shargie and Lindtjørn, 2007) transmission-aware models can be used to infer HIV evolutionary rates ( Vrancken et al., 2014).Ī series of events in which an infected individual infects another individual can be shown as a transmission network, which itself is a subset of a contact network, a graph in which nodes represent individuals and edges represent contacts (e.g. The spread of many infectious diseases is driven by social and sexual networks ( Kelly et al., 1991), and reconstructing their transmission histories from molecular data may be able to enhance intervention.
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