Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India.
Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India.
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DOI：
10.1063/5.0016240
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发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Sarkar K
中科院分区：
文献类型：
--
作者：
Khajanchi S;Sarkar K
持续的新型冠状病毒疫情于二零二零年三月十一日被世界卫生组织宣布为大流行，而印度政府于二零二零年三月二十五日宣布全国封锁，以防止新型冠状病毒病（COVID-19）的社区传播。由于缺乏特定的抗病毒药物或疫苗，数学建模在更好地理解疾病动力学和设计控制快速传播的传染病的策略方面发挥着重要作用。在我们的研究中，我们开发了一个新的房室模型，解释了COVID-19的传播动力学。我们用印度四个邦的每日COVID-19数据校准了我们提出的模型，即贾坎德、古吉拉特邦、安得拉邦和昌迪加尔。我们研究了模型的定性性质，包括可行平衡点及其关于基本再生数的稳定性。当感染者的恢复率增加时，无病平衡点变得稳定，地方病平衡点变得不稳定，但如果疾病传播率保持较高，则地方病平衡点始终保持稳定。对于所有四个州的估计模型参数，这表明COVID-19的重大爆发。短期预测显示，印度四个邦的COVID-19每日和累积病例呈上升趋势。
The ongoing novel coronavirus epidemic was announced a pandemic by the World Health Organization on March 11, 2020, and the Government of India declared a nationwide lockdown on March 25, 2020 to prevent community transmission of the coronavirus disease (COVID)-19. Due to the absence of specific antivirals or vaccine, mathematical modeling plays an important role in better understanding the disease dynamics and in designing strategies to control the rapidly spreading infectious disease. In our study, we developed a new compartmental model that explains the transmission dynamics of COVID-19. We calibrated our proposed model with daily COVID-19 data for four Indian states, namely, Jharkhand, Gujarat, Andhra Pradesh, and Chandigarh. We study the qualitative properties of the model, including feasible equilibria and their stability with respect to the basic reproduction number . The disease-free equilibrium becomes stable and the endemic equilibrium becomes unstable when the recovery rate of infected individuals increases, but if the disease transmission rate remains higher, then the endemic equilibrium always remains stable. For the estimated model parameters, for all four states, which suggests the significant outbreak of COVID-19. Short-time prediction shows the increasing trend of daily and cumulative cases of COVID-19 for the four states of India.
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影响因子：
168.9
作者：
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通讯作者：
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DOI：
10.1016/j.physa.2018.01.014
发表时间：
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影响因子：
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作者：
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根据早期数据预测中国 COVID-19 疫情的累计病例数
影响因子：
2.6
作者：
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Webb, Glenn