Rakshit Pranati, Kumar Soumen, Noeiaghdam Samad, Fernandez-Gamiz Unai, Altanji Mohamed, Santra Shyam Sundar
Department of Computer Science and Engineering, JIS College of Engineering, Kalyani, West Bengal, India.
Associate Consultant and Data Scientist in Tata Consultancy Services Ltd., Kolkata, West Bengal, India.
Results Phys. 2022 Sep;40:105855. doi: 10.1016/j.rinp.2022.105855. Epub 2022 Jul 27.
Corona virus disease 2019 (COVID-19) is an infectious disease and has spread over more than 200 countries since its outbreak in December 2019. This pandemic has posed the greatest threat to global public health and seems to have changing characteristics with altering variants, hence various epidemiological and statistical models are getting developed to predict the infection spread, mortality rate and calibrating various impacting factors. But the aysmptomatic patient counts and demographical factors needs to be considered in model evaluation. Here we have proposed a new seven compartmental model, Susceptible- Exposed- Infected-Asymptomatic-Quarantined-Fatal-Recovered (SEIAQFR) which is based on classical Susceptible-Infected-Recovered (SIR) model dynamic of infectious disease, and considered factors like asymptomatic transmission and quarantine of patients. We have taken UK, US and India as a case study for model evaluation purpose. In our analysis, it is found that the Reproductive Rate ( ) of the disease is dynamic over a long period and provides better results in model performance ( R-square score) when model is fitted across smaller time period. On an average cases are asymptomatic and have contributed to model accuracy. The model is employed to show accuracy in correspondence with different geographic data in both wave of disease spread. Different disease spreading factors like infection rate, recovery rate and mortality rate are well analyzed with best fit of real world data. Performance evaluation of this model has achieved good R-Square score which is for infection prediction and for death prediction and an average MAPE in different wave of the disease in UK, US and India.
2019冠状病毒病（COVID-19）是一种传染病，自2019年12月爆发以来已蔓延至200多个国家。这场大流行对全球公共卫生构成了最大威胁，而且随着病毒变种的变化似乎具有不断变化的特征，因此正在开发各种流行病学和统计模型来预测感染传播、死亡率并校准各种影响因素。但是在模型评估中需要考虑无症状患者数量和人口统计学因素。在此，我们提出了一种新的七分区模型，即易感-暴露-感染-无症状-隔离-死亡-康复（SEIAQFR）模型，该模型基于传染病的经典易感-感染-康复（SIR）模型动态，并考虑了无症状传播和患者隔离等因素。我们以英国、美国和印度为例进行模型评估。在我们的分析中发现，该疾病的繁殖率（ ）在很长一段时间内是动态的，并且当模型在较短时间段内拟合时，在模型性能（ 决定系数得分）方面能提供更好的结果。平均而言， 病例为无症状病例，这有助于提高模型的准确性。该模型用于展示在疾病传播的两个阶段与不同地理数据相对应的准确性。通过对实际数据的最佳拟合，对感染率、康复率和死亡率等不同的疾病传播因素进行了很好的分析。该模型的性能评估取得了良好的决定系数得分，在英国、美国和印度疾病的不同阶段，感染预测的得分是 ，死亡预测的得分是 ，平均平均绝对百分比误差为 。
