Bandekar Shraddha Ramdas, Ghosh Mini
Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India.
Eur Phys J Plus. 2021;136(10):1058. doi: 10.1140/epjp/s13360-021-02046-y. Epub 2021 Oct 21.
The pandemic started in the late 2019 and is still waving in claiming millions of lives with virus being mutated to deadlier form. This pandemic has caught attention toward interventions like improved detection of the infected, better quarantine facilities and adequate medical facilities in terms of hospital beds and other medical aid. In this study, we developed a 7-compartment epidemiological model, with inclusion of identified and unidentified infected population along with media factor associated with the aware identified infected population. This is included by using Holling function in the nonlinear incidence, that is responsible for reduction in infection rate via identified infected. The model is fitted to the observed active COVID-19 cases data, collected for a period of 11 months between July 2020 to May 2021 of Nepal and India, and the infection rate as well as the basic reproduction number is obtained for the first wave and second wave of the pandemic in both countries. A comparative analysis on the effect of different parameters on the disease prevalence for both the countries is presented in this work. Sensitivity analysis, time series behavior and optimal control analysis with control parameters equating with reduced infection rate, enhanced detection rate, improved quarantine and hospitalization rate are presented in detail. By means of PRCC, sensitivity analysis is performed and the key parameters influencing the disease prevalence are identified. A detailed study on impact of several parameters in the COVID-19 prevalence, thereby suggesting the interventions to be implemented is discussed in the work. Predictions till June 30, 2021, are obtained using the second wave data for both the countries, and a declining trend is observed for both the countries for the next 30 to 40 days. The estimated values of the infection rates and the hospitalization rates obtained are higher for India compared to Nepal. An optimal control analysis for both the countries is described in detail providing the difference in infectives and recoveries with and without any controls or interventions. The study suggests that improved treatment facilities, testing drives and other non-pharmaceutical interventions would bring down the infected cases to a major extent.
这场大流行始于2019年末，至今仍在肆虐，随着病毒变异为更致命的形式，已夺去数百万人的生命。这场大流行引发了人们对一些干预措施的关注，比如改进对感染者的检测、改善隔离设施以及在病床和其他医疗援助方面提供充足的医疗设施。在本研究中，我们开发了一个七房室流行病学模型，纳入了已识别和未识别的感染人群，以及与已识别且知晓的感染人群相关的媒体因素。这是通过在非线性发病率中使用Holling函数实现的，该函数负责通过已识别的感染者降低感染率。该模型拟合了2020年7月至2021年5月期间为尼泊尔和印度收集的为期11个月的观察到的活跃新冠肺炎病例数据，并得出了两国大流行第一波和第二波的感染率以及基本再生数。本文对不同参数对两国疾病流行率的影响进行了比较分析。详细介绍了敏感性分析、时间序列行为以及控制参数等同于降低感染率、提高检测率、改善隔离和住院率的最优控制分析。通过PRCC进行敏感性分析，并确定了影响疾病流行率的关键参数。本文详细讨论了几个参数对新冠肺炎流行率的影响，从而提出了有待实施的干预措施。利用两国的第二波数据获得了截至2021年6月
