TY - JOUR TI - Integrating genomic and epidemiologic data to accelerate progress toward schistosomiasis elimination AU - Lund, Andrea J AU - Wade, Kristen J AU - Nikolakis, Zachary L AU - Ivey, Kathleen N AU - Perry, Blair W AU - Pike, Hamish NC AU - Paull, Sara H AU - Liu, Yang AU - Castoe, Todd A AU - Pollock, David D AU - Carlton, Elizabeth J A2 - van der Meer, Jos W VL - 11 PY - 2022 DA - 2022/08/30 SP - e79320 C1 - eLife 2022;11:e79320 DO - 10.7554/eLife.79320 UR - https://doi.org/10.7554/eLife.79320 AB - The global community has adopted ambitious goals to eliminate schistosomiasis as a public health problem, and new tools are needed to achieve them. Mass drug administration programs, for example, have reduced the burden of schistosomiasis, but the identification of hotspots of persistent and reemergent transmission threaten progress toward elimination and underscore the need to couple treatment with interventions that reduce transmission. Recent advances in DNA sequencing technologies make whole-genome sequencing a valuable and increasingly feasible option for population-based studies of complex parasites such as schistosomes. Here, we focus on leveraging genomic data to tailor interventions to distinct social and ecological circumstances. We consider two priority questions that can be addressed by integrating epidemiological, ecological, and genomic information: (1) how often do non-human host species contribute to human schistosome infection? and (2) what is the importance of locally acquired versus imported infections in driving transmission at different stages of elimination? These questions address processes that can undermine control programs, especially those that rely heavily on treatment with praziquantel. Until recently, these questions were difficult to answer with sufficient precision to inform public health decision-making. We review the literature related to these questions and discuss how whole-genome approaches can identify the geographic and taxonomic sources of infection, and how such information can inform context-specific efforts that advance schistosomiasis control efforts and minimize the risk of reemergence. KW - schistosomiasis KW - population genetics KW - surveillance KW - one health KW - disease control KW - whole-genome sequencing JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -