Epidemic analysis of COVID-19 in China by dynamical modeling

By Liangrong Peng, Wuyue Yang, Dongyang Zhang, Changjing Zhuge, L. Hong. medRxiv, 2020

620 citations recorded by the source corpus.

The outbreak of the novel coronavirus (2019-nCoV) epidemic has attracted world- wide attention. Herein, we propose a mathematical model to analyzes this epidemic, based on a dynamic mechanism that incorporating the intrinsic impact of hidden la- tent and infectious cases on the entire process of transmission. Meanwhile, this model is validated by data correlation analysis, predicting the recent public data, and back- tracking, as well as sensitivity analysis. The dynamical model reveals the impact of various measures on the key parameters of the epidemic. According to the public data of NHCs from 01/20 to 02/09, we predict the epidemic peak and possible end time for 5 different regions. The epidemic in Beijing and Shanghai, Mainland/Hubei and Hubei/Wuhan, are expected to end before the end of February, and before mid- March respectively. The model indicates that, the outbreak in Wuhan is predicted to be ended in the early April. As a result, more effective policies and more efforts on clinical research are demanded. Moreover, through the backtracking simulation, we infer that the outbreak of the epidemic in Mainland/Hubei, Hubei/Wuhan, and Wuhan can be dated back to the end of December 2019 or the beginning of January 2020.

Subfields: Economics and Econometrics, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5548ebccfc6c40ccbd68aa20c210661aa92639ba

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Citations in the corpus

• Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis (2020, background): “sion of the disease and its contagiousness, which makes the condition more complicated (19, 20).”
• Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study (2020, neutral): “31 31 Very recently, in [8], a method based generalized SEIR model has been developed by incorporating quarantined and 32 32 recovery states to predict and analyze the COVID-19 epidemic.”
• Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network. (2020, background): “Considerable number of analysis of the available data of the number of cases and deaths have been attempted recently, and a few data driven models have also been proposed [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] .”
• A Time-dependent SIR model for COVID-19 (2020, neutral): “, [4], [5], [6], and [7] 固定某些關 鍵參數如 transmission rate 與 recovering rate ，我們的 transmission rate 與 recovering rate 會基於過往的資料進 transmission rate 驟降。除 了基於各式傳染病模型的論文，也有許多預測 COVID-19 趨勢論文採用 data-driven 或是 curve fitting 方法 [8], [9], and [10] 其中，上述微分方程組仍滿足 Eq.”