Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
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DOI：
10.1016/j.idm.2020.08.001
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发表时间：
2020-03
影响因子：
8.8
通讯作者：
Qihui Yang;Chunlin Yi;A. Vajdi;L. Cohnstaedt;Hongyu Wu;Xiaolong Guo;C. Scoglio
中科院分区：
文献类型：
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作者：
Qihui Yang;Chunlin Yi;A. Vajdi;L. Cohnstaedt;Hongyu Wu;Xiaolong Guo;C. Scoglio
作为一种新兴传染病，2019冠状病毒病（COVID-19）已发展成为全球大流行病。在病毒在中国的最初传播期间，我们证明了集合卡尔曼滤波器作为武汉市每日报告病例的短期预测器表现良好。其次，我们使用基于个人水平的网络模型来重建湖北省的流行动力学，并在各种情景下检验非药物干预对流行病传播的有效性。我们的模拟结果表明，如果没有持续的控制措施，湖北省的疫情可能会持续下去。只有通过1）保护措施和2）社会距离继续降低感染率，才能在模拟中重建湖北省发生的实际疫情轨迹。最后，我们用非马尔可夫过程模拟了COVID-19的传播，并展示了这些模型如何产生不同的流行轨迹，与马尔可夫过程相比。由于最近的研究表明，COVID-19流行病学参数不遵循导致马尔可夫过程的指数分布，未来的工作需要关注非马尔可夫模型，以更好地捕捉COVID-19传播轨迹。此外，通过早期病例识别和隔离缩短传染期可以显著减缓疫情蔓延。
As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.