Genetic and social network analysis to target interventions for malaria elimination

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PROJECT SUMMARY/ABSTRACT This proposed K01 award will support the career development of Dr. Jennifer Smith, an Assistant Adjunct Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF). Dr. Smith's career goal is to become an independent researcher with combined expertise in parasite genotyping and human network analyses to optimize interventions for infectious disease elimination. To support her career development, this application proposes a study that leverages data collected as part of ongoing research in malaria high-risk populations and uses novel genetic and social network analyses to address an urgent challenge preventing achievement of malaria elimination targets. As malaria transmission declines, an increasingly large proportion of the parasite reservoir is clustered in specific sub-populations with high exposure to infection and who often face significant barriers to accessing and utilizing malaria interventions. While normative bodies like the World Health Organization recommend a targeted response in known malaria high-risk populations, there is limited evidence on the extent to which these populations drive transmission, the impact of targeted interventions or how to optimize coverage. Through cross-sectional and temporal analysis of genetic and social network data collected as part of an existing, separately funded population-based evaluation of targeted malaria interventions in high-risk populations, this K01 proposes to investigate genetic connectivity between infections in migrant and resident populations and the role social networks play in uptake of malaria interventions. The specific aims are to (1) quantify parasite genetic connectivity and transmission potential within and between migrant and resident populations at different time points and spatial scales, (2) evaluate the influence of social network attributes on uptake of malaria prevention measures, and (3) model transmission networks and estimate the impact of alternative intervention strategies in migrant and resident agricultural workers. This study will provide crucial knowledge on how malaria high-risk populations contribute to transmission dynamics, inform how social networks can be leveraged to improve intervention uptake, and quantify the impact of targeted interventions on overall transmission. The proposed research will build on Dr. Smith's foundation in epidemiologic methods and include a 5-year training plan including mentorship from leaders in genetic and malaria epidemiology, social network analysis and mathematical modelling at UCSF, University of Southern California and UC Berkeley. Dr. Smith's training goals are to (1) gain knowledge in malaria genetic epidemiology and applied analytic approaches for genetic data, (2) develop expertise in advanced social network theory and analytic methods, and (3) obtain training in mathematical modelling. The findings will be used as a foundation for an R01 to implement and evaluate network-based interventions among malaria high-risk populations in northern Namibia.