Bandekar Shraddha Ramdas, Ghosh Mini
Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India.
Math Comput Simul. 2022 Oct;200:1-31. doi: 10.1016/j.matcom.2022.04.001. Epub 2022 Apr 16.
COVID-19 had been declared a public health emergency by the World Health Organization in the early 2020. Since then, this deadly virus has claimed millions of lives worldwide. Amidst its chaotic spread, several other diseases have faced negligence in terms of treatment and care, of which one such chronic disease is Tuberculosis. Due to huge rise in COVID-19 cases, there had been a drastic decrease in notification of TB cases which resulted in reversal of global TB target progress. Apart from these due to the earlier co-infections of TB with SARS and MERS-CoV viruses, the TB-COVID-19 co-infection posed a severe threat in the spread of the disease. All these factors backed to be major motivation factor in development of this model. Leading with this concern, a TB - COVID-19 co-infection model is developed in this study, considering possibility of waning immunity of both diseases. Considering different epidemiological traits, an epidemiological model with 11 compartments is developed and the co-dynamics is analysed. A detailed stability and bifurcation analysis is performed for the TB only sub-model, COVID-19 only sub-model and the complete TB - COVID-19 model. Impact of key parameters namely, infection rate, waning immunity, and face mask efficacy on disease prevalence is discussed in detail. Sensitivity analysis by means of normalized forward sensitivity index of the basic reproduction number and LHS-PRCC approach is carried to provide a thorough understanding of significance of various parameters in accelerating as well as controlling the disease spread. Optimal control analysis is presented extensively, incorporating controls related to timely and improved TB treatment, and enhanced COVID-19 tests and isolation facilities to curb the spread of these infectious diseases. The simulation results obtained from each of these analyses stress on the importance of different control measures in mitigation of the diseases and are illustrated accordingly. The study suggests that in the times of a pandemic, other disease treatment and care must not be neglected, and adequate care must be taken so that mortality due to co-infection and unavailability of timely treatment can be avoided.
2020年初，世界卫生组织已宣布新冠疫情为突发公共卫生事件。自那时起，这种致命病毒已在全球夺走数百万人的生命。在其肆虐传播期间，其他几种疾病在治疗和护理方面受到忽视，其中一种慢性病就是结核病。由于新冠病例大幅增加，结核病病例通报大幅减少，导致全球结核病目标进展出现逆转。此外，由于结核病此前曾与SARS和MERS-CoV病毒合并感染，结核病与新冠病毒合并感染对疾病传播构成严重威胁。所有这些因素成为开发此模型的主要推动因素。出于这种担忧，本研究开发了一种结核病与新冠病毒合并感染模型，考虑了两种疾病免疫力下降的可能性。考虑到不同的流行病学特征，开发了一个具有11个区室的流行病学模型，并对其共同动态进行了分析。对仅结核病子模型、仅新冠病毒子模型以及完整的结核病与新冠病毒模型进行了详细的稳定性和分岔分析。详细讨论了关键参数即感染率、免疫力下降和口罩功效对疾病流行率的影响。通过基本再生数的归一化正向敏感性指数和LHS-PRCC方法进行敏感性分析，以全面了解各种参数在加速和控制疾病传播中的重要性。广泛进行了最优控制分析，纳入了与及时和改善结核病治疗以及加强新冠病毒检测和隔离设施相关的控制措施，以遏制这些传染病的传播。从这些分析中获得的模拟结果强调了不同控制措施在减轻疾病方面的重要性，并相应地进行了说明。该研究表明，在大流行时期，其他疾病的治疗和护理绝不能被忽视，必须采取充分的护理措施，以避免因合并感染和无法及时治疗而导致的死亡。
