低温或高隔离延迟增加了印度的平均COVID-19感染:一种数学建模方法

D. Prakash, Bishal Chhetri, D. Vamsi, S. Balasubramanian, C. Sanjeevi
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引用次数: 0

摘要

通过将人群分为五个隔间,使用一组延迟微分方程捕获了印度COVID-19的动态。证明了系统的正性和有界性。给出了方程组解的存在唯一性条件。计算了系统的平衡点,并进行了稳定性分析。对模型参数进行了敏感性分析。进行了分岔分析,计算了临界时延。通过将扩散参数表示为温度的函数,研究了温度对种群的影响。我们的结论是,随着气温的下降,人群中的平均感染率上升。鉴于印度即将到来的冬季,新感染病例将会增加。该模型符合隔离时间延长会增加人群平均感染的特征。
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Low temperatures or high isolation delay increases the average COVID-19 infections in India : A Mathematical modeling approach
Abstract The dynamics of COVID-19 in India are captured using a set of delay differential equations by dividing a population into five compartments. The Positivity and Boundedness of the system is shown. The Existence and Uniqueness condition for the solution of system of equations is presented. The equilibrium points are calculated and stability analysis is performed. Sensitivity analysis is performed on the parameters of the model. Bifurcation analysis is performed and the critical delay is calculated. By formulating the spread parameter as a function of temperature, the impact of temperature on the population is studied. We concluded that with the decrease in temperature, the average infections in the population increases. In view of the coming winter season in India, there will be an increase in new infections. This model falls in line with the characteristics that increase in isolation delay increases average infections in the population.
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来源期刊
Computational and Mathematical Biophysics
Computational and Mathematical Biophysics Mathematics-Mathematical Physics
CiteScore
2.50
自引率
0.00%
发文量
8
审稿时长
30 weeks
期刊最新文献
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