Selected article for: "data set and stochastic model"

Author: Sanyi Tang; Biao Tang; Nicola Luigi Bragazzi; Fan Xia; Tangjuan Li; Sha He; Pengyu Ren; Xia Wang; Zhihang Peng; Yanni Xiao; Jianhong Wu
Title: Stochastic discrete epidemic modeling of COVID-19 transmission in the Province of Shaanxi incorporating public health intervention and case importation
  • Document date: 2020_2_29
  • ID: aoqyx8mk_5
    Snippet: Our analysis is based on the complete "tracking and recording" information of imported cases and local cases detected in the province of Shaanxi province [7] , as this allows us to generate a data set that includes the time series of daily latent, infected and hospitalized cases. Since the outbreak scale in the Province of Shanxi was relatively small and this is subject to stochasticity in both case importation and local cases generated by the im.....
    Document: Our analysis is based on the complete "tracking and recording" information of imported cases and local cases detected in the province of Shaanxi province [7] , as this allows us to generate a data set that includes the time series of daily latent, infected and hospitalized cases. Since the outbreak scale in the Province of Shanxi was relatively small and this is subject to stochasticity in both case importation and local cases generated by the importation and since the Provincial data has detailed information on symptom onset and serial intervals, we adopt a method developed by Nishiura and Chowell [15, 16] to estimate the effective reproduction number, and we revise some of our early deterministic models to a novel discrete stochastic model to address the key issues relevant to preventing a secondary outbreak of COVID-19 in other provinces except for Hubei, due to the anticipated gradually relaxing of travel restriction and returning to normal social and economic activities.

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