In this paper, we develop an extension of standard epidemiological models, suitable for COVID-19. This extension incorporates the transmission due to pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model also captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. The model describes the probabilistic rise in the number of confirmed cases due to the concomitant effects of (incipient) human transmission and multiple compartments. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. For instance, this model demonstrates that increasing the testing for symptomatic patients does not have any major effect on the progression of the pandemic, but testing rate of the asymptomatic population has an extremely crucial role to play. The model is executed using the data obtained for the state of Chhattisgarh in the Republic of India. The model is shown to have significantly better predictive capability than the other epidemiological models. This model can be readily applied to any administrative boundary (state or country). Moreover, this model can be applied for any other epidemic as well.
翻译:在本文中,我们开发了适合COVID-19的标准流行病学模型的扩展。这一扩展包括了由于病毒的症状前或无症状携带者的传播;此外,这一模型还记录了由于人员在一国不同行政边界之间流动而导致的疾病蔓延;该模型描述了由于(初级)人类传播和多包厢的附带影响而确诊病例的概率上升;该模型的相关参数可以帮助设计公共卫生政策和该流行病的业务管理;例如,这一模型表明,增加对症状患者的检测不会对流行病的蔓延产生任何重大影响,但无症状人群的检测率可以发挥极其重要的作用;该模型使用印度共和国查蒂斯加尔州获得的数据进行实施;该模型的预测能力大大高于其他流行病学模型。这一模型可以很容易适用于任何行政边界(州或国家),此外,这一模型也可以适用于任何其他流行病。