论文标题
预测印度共同19日大流行的每日和累积案件数量
Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India
论文作者
论文摘要
世界卫生组织于2020年3月11日和政府宣布了正在进行的小说冠状病毒流行病。印度从2020年3月25日开始宣布全国范围内的封锁，以防止社区传播Covid-19。由于没有特定的抗病毒药或疫苗，数学建模起着重要的作用，可以更好地了解疾病动态和设计迅速传播感染性疾病的策略。在我们的研究中，我们开发了一种新的隔间模型，该模型解释了Covid-19的传播动力学。我们使用每日Covid-19校准了我们提出的模型，该模型为四个印度省，即Jharkhand，Gujarat，Andhra Pradesh和Chandigarh校准。我们研究模型的定性属性，包括可行的平衡及其相对于基本复制号$ \ Mathcal {r} _0 $的稳定性。当感染个体的恢复速率增加时，无疾病的平衡变得稳定，地方性平衡变得不稳定，但是如果疾病传播率保持较高，那么流行均衡始终保持稳定。对于估计的模型参数，所有四个省份的$ \ Mathcal {r} _0> 1 $，这表明COVID-19的重大爆发。短期预测表明，印度四个省的Covid-19每日和累积案例的趋势不断增加。
The ongoing novel coronavirus epidemic has been announced a pandemic by the World Health Organization on March 11, 2020, and the Govt. of India has declared a nationwide lockdown from March 25, 2020, to prevent community transmission of COVID-19. Due to absence of specific antivirals or vaccine, mathematical modeling play an important role to better understand the disease dynamics and designing strategies to control rapidly spreading infectious diseases. 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 the four Indian provinces, 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 $\mathcal{R}_0$. The disease-free equilibrium becomes stable and the endemic equilibrium becomes unstable when the recovery rate of infected individuals increased but if the disease transmission rate remains higher then the endemic equilibrium always remain stable. For the estimated model parameters, $\mathcal{R}_0 >1$ for all the four provinces, 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 provinces of India.
