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Browsing by Author "Xie, Min"

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    Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach
    Lin, Chun-Pang; Dorigatti, Ilaria; Tsui, Kwok-Leung; Xie, Min; Ling, Man-Ho; Yuan, Hsiang-Yu (Frontiers, 2022-11)
    BackgroundBefore major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. MethodsWe estimated the effective reproduction number (R-t) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified. FindingsThe model-estimated mean R-t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R-t could have been less than 1. ConclusionOur model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.
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    Signal decomposition for nonstationary processes
    Xie, Min (Virginia Tech, 1995)
    The main purpose of this dissertation is to explore and develop better signal modeling (decomposition) methods for nonstationary and/or nonlinear dynamic processes. Localization is the main focus. The characteristics of a nonstationary or nonlinear signal are decomposed onto a set of basis functions, either in the phase space spanned by time-frequency coordinates as Gabor proposed, or in the phase space spanned by a set of derivatives of different degree as defined in physics. To deal with time-varying signals, a Multiresolution Parametric Spectral Estimator (MPSE) is proposed together with its theory, techniques and applications. The resolution study provides the characteristics of windowed Fourier transforms, wavelet transforms, fixed resolution parametric spectral estimators, and the newly developed MPSE. Both the theoretical and the experimental results show that, of the above techniques, MPSE is the best in resolution. Furthermore, with proper a priori knowledge, MPSE can yield better resolution than the lower bound defined by the Heisenberg uncertainty principle. The application examples demonstrate the great potential of the MPSE method for tracking and analyzing time-varying processes. To deal with the time-varying characteristics caused by linearization of nonlinear processes, the Radial Basis Function Network (RBFN) is proposed for modeling nonlinear processes from a 'local' to a 'global' level. An equal distance sample rule is proposed for constructing the RBEN. Experiments indicate that the RBFN is a promising method for modeling deterministic chaos as well as stochastic processes, be it linear or nonlinear. The 'local' to 'global' approach of the RBEN also provides great potential for structure adaptation and knowledge accumulation.