Improving leprosy prevention strategies by integrating social network analysis with spatial and molecular epidemiology data of Mycobacterium leprae in the Comoros

Improving leprosy prevention strategies by integrating social network analysis with spatial and molecular epidemiology data of Mycobacterium leprae in the Comoros.

Leprosy, an infectious disease transmitted by Mycobacterium leprae, has been known since biblical times. Although it is assumed that humans transmit the disease to others humans, it is still poorly understood how this happens exactly. In order to improve leprosy prevention and control strategies, we therefore propose to complement spatial-epidemiology data and phylogenetic trees emerging from an on-going cluster randomized trial in the Comoros (PEOPLE) with the analysis of social networks.
We will study the social networks of entire villages using a mixed-methods study design combining qualitative and quantitative research methods. We will further create an interdisciplinary platform to integrate insights from spatial-epidemiological, molecular, and social science research using computational network analysis, presenting us with a unique opportunity to improve our knowledge of the complex transmission dynamics in this setting, By understanding how transmission works, we will know who benefits most from leprosy screening within the patient's social network - beyond those living nearest.
Based on this research, we will be able to formulate concrete recommendations to improve leprosy prevention and control efforts in the Comoros, as well as more generalizable recommendations that could benefit leprosy interventions globally.