Ben Singer is a postdoctoral scholar with interests in mathematical epidemiology and global public health. Ben's research career began with an internship at the Okinawa Institute of Science and Technology, where he applied quantitative skills he had learnt studying physics at the University of Oxford to the study of nematode locomotion. Ben further pursued quantitative methods in life sciences in the Interdisciplinary Bioscience Doctoral Training Partnership at the University of Oxford, earning a DPhil (PhD equivalent) in mathematical methods for evaluating pandemic risk and control. During these studies he maintained an interest in global public health policy, interning with the UK government's Department for International Development, where he developed models of international COVID-19 vaccine distribution. Ben is now working in Nathan Lo's research group at Stanford, creating infectious disease models informing public health policy for schistosomiasis, hepatitis E, and other infections.

Stanford Advisors

All Publications

  • Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration. Proceedings of the National Academy of Sciences of the United States of America Singer, B. J., Coulibaly, J. T., Park, H. J., Andrews, J. R., Bogoch, I. I., Lo, N. C. 2024; 121 (2): e2315463120


    Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.

    View details for DOI 10.1073/pnas.2315463120

    View details for PubMedID 38181058