Browsing by Author "Chen, Yu"

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    Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations.
    (Royal Society open science, 2021-07-28) Chen, Yu; Cheng, Jin; Gupta, Arvind; Huang, Huaxiong; Xu, Shixin
    Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ordinary differential equations with partial observations. Our method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms. By using initial values obtained by Bayesian steps with low sampling numbers, our deterministic optimization algorithm is both accurate, robust and efficient with partial observations and large noise.
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    Reduction in urinary arsenic levels in response to arsenic mitigation efforts in Araihazar, Bangladesh.
    (Environ Health Perspect, 2007-06) Chen, Yu; van Geen, Alexander; Graziano, Joseph H; Pfaff, Alexander; Madajewicz, Malgosia; Parvez, Faruque; Hussain, AZM Iftekhar; Slavkovich, Vesna; Islam, Tariqul; Ahsan, Habibul
    BACKGROUND: There is a need to identify and evaluate an effective mitigation program for arsenic exposure from drinking water in Bangladesh. OBJECTIVE: We evaluated the effectiveness of a multifaceted mitigation program to reduce As exposure among 11,746 individuals in a prospective cohort study initiated in 2000 in Araihazar, Bangladesh, by interviewing participants and measuring changes in urinary As levels. METHODS: The interventions included a) person-to-person reporting of well test results and health education; b) well labeling and village-level health education; and c) installations of 50 deep, low-As community wells in villages with the highest As exposure. RESULTS: Two years after these interventions, 58% of the 6,512 participants with unsafe wells (As >/=50 microg) at baseline had responded by switching to other wells. Well labeling and village-level health education was positively related to switching to safe wells (As < 50 mug/L) among participants with unsafe wells [rate ratio (RR) = 1.84; 95% confidence interval (CI), 1.60-2.11] and inversely related to any well switching among those with safe wells (RR = 0.80; 95% CI, 0.66-0.98). The urinary As level in participants who switched to a well identified as safe (< 50 microg As/L) dropped from an average of 375 microg As/g creatinine to 200 microg As/g creatinine, a 46% reduction toward the average urinary As content of 136 microg As/g creatinine for participants that used safe wells throughout. Urinary As reduction was positively related to educational attainment, body mass index, never-smoking, absence of skin lesions, and time since switching (p for trend < 0.05). CONCLUSIONS: Our study shows that testing of wells and informing households of the consequences of As exposure, combined with installation of deep community wells where most needed, can effectively address the continuing public health emergency from arsenic in drinking water in Bangladesh.