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An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19

机译:一种综合确定性 - 随机预测Covid-19长期轨迹的预测

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The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic-stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible-Infected-Recovered-Protected-Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be 4.78 and 3.25, respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020 9 June, 2020 will be 10,982 (6383-15,582) and 13,731 (3395-29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0 183) and 92 (0-602), respectively.
机译:2019年正在进行的冠状病毒疾病(Covid-19)大流行是几十年的主要健康紧急情况之一,这影响了世界上几乎每个国家。截至2020年6月30日,它造成了超过1000万确诊感染的爆发,全球超过500,000人死亡。由于有效治疗(或疫苗)的不可用,有关流行病的传输机制的证据不足,世界人口目前处于脆弱的位置。对于深刻影响国家的Covid-19日常情况数据集代表了由确定性和随机组件组成的随机过程。本研究提出了一种综合的确定性 - 随机方法,以预测意大利和西班牙的Covid-19案件的长期轨迹。通过SIR的扩展版本评估每日案例单变量时间序列的确定性分量[易感感染恢复保护的(SIRCX)]模型,而其随机分量是使用自回归(AR)时间序列进行建模的模型。基于两个可操作的SIRCX-AR(ISA)方法的建议集成SIRCX-AR(ISA)方法利用了确定性SIRCX和随机AR模型的优越性,以找到流行病曲线的长期轨迹。基于所提出的ISA模型的实验分析显示出意大利和西班牙的Covid-19案件的长期预测,与基于颂的SIRCX模型的长期预测。基于SIRCX模型的意大利和西班牙的估计基本再现号码分别为4.78和3.25。基于ISA模型的结果表明,意大利和西班牙的案件数量分别为10,982(6383-15,582)和13,731(3395-29,013)。此外,意大利和西班牙7月9日2020年7月9日的预期每日病例数分别为30(0183)和92(0-602)。

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