Social network analysis
Recent innovations in molecular biology and developments in network science present a unique opportunity to our understanding of leprosy transmission dynamics to optimize its prevention and control. Although increased risk of infection has been shown for individuals living in close contact with leprosy patients, there are multiple difficulties to document transmission pathways (including long incubation periods and large percentage of asymptomatic infection). Clearly apparent from on-going research is that geographical distance can only partially explain transmission. New insights indicate that many new patients cannot be spatially linked to an index case among their household (HH) contacts or direct neighbours, meaning that other types of relations constitute the missing link for effectively understanding transmission dynamics and intervention strategies. Researchers at ITM are currently applying quantitative and qualitative methods to describe the role of social networks in leprosy transmission. Our assumption is that on the causal pathway, contact and network data are closer to the outcome ‘leprosy’ than spatial data and that valuable insights can be gained by including them. The Data Hub supports the development of methods that can increase reliability of data collected through the general framework of quantitative social network analysis (SNA) in a context of high annual leprosy incidence.
Started in 2020 under Structural Research Funding (SOFI) at ITM