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_19
Snippet: On the basis of the clinical progression of the disease, epidemiological status of the individuals, and intervention measures we stratify the populations as susceptible ( ), exposed ( ), infected ( ), hospitalized ( ), recovered ( ) compartments, the quarantined susceptible ( ) and quarantined suspected individuals ( ). The flow diagram is shown in Figure 3 . Note that here the model framework extends the model structure in our previous study by .....
Document: On the basis of the clinical progression of the disease, epidemiological status of the individuals, and intervention measures we stratify the populations as susceptible ( ), exposed ( ), infected ( ), hospitalized ( ), recovered ( ) compartments, the quarantined susceptible ( ) and quarantined suspected individuals ( ). The flow diagram is shown in Figure 3 . Note that here the model framework extends the model structure in our previous study by including the quarantined suspected compartment [18] [19] [20] , which consists of exposed infectious individuals resulting from contact tracing and individuals with influenza like illness and common fever needing clinical medication. The deterministic model is replaced by a discrete system with stochastic importation since the case numbers are relatively small with most of the early cases being imported in Shaanxi province [10, 12] . This modelling approach can well describe demographic stochasticity. The
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