Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by leveraging the strengths of the grey-box system theory or grey-box identification, known for its robustness in problem solving under partial, incomplete, or uncertain data. Empirical data on confirmed cases and deaths, extracted from an open source repository were used to develop the SEAIRD compartment model. Adjustments were made to fit current knowledge on the COVID-19 behavior. The model was implemented and solved using an Ordinary Differential Equation solver and an optimization tool. A cross-validation technique was applied, and the coefficient of determination $R^2$ was computed in order to evaluate the goodness-of-fit of the model. %to the data. Key epidemiological parameters were finally estimated and we provided the rationale for the construction of SEAIRD model. When applied to Brazil's cases, SEAIRD produced an excellent agreement to the data, with an %coefficient of determination $R^2$ $\geq 90\%$. The probability of COVID-19 transmission was generally high ($\geq 95\%$). On the basis of a 20-day modeling data, the incidence rate of COVID-19 was as low as 3 infected cases per 100,000 exposed persons in Brazil and France. Within the same time frame, the fatality rate of COVID-19 was the highest in France (16.4\%) followed by Brazil (6.9\%), and the lowest in Russia ($\leq 1\%$). SEAIRD represents an asset for modeling infectious diseases in their dynamical stable phase, especially for new viruses when pathophysiology knowledge is very limited.
翻译:COVID-19的常见区划模型基于先验知识和许多假设。 此外,这些模型没有系统地纳入无症状案例。我们的研究旨在通过利用灰盒系统理论或灰盒识别的优势,为数据驱动方法提供一个框架,利用灰盒系统理论或灰盒识别的优势,以其在部分、不完整或不确定数据下解决问题的稳健性而闻名。从开放源库提取的关于已确认病例和死亡的经验数据用于开发SEAIRD区划模型。调整是为了适应目前对COVID-19行为的知识。模型的实施和解决使用了普通差异化解析器和优化工具。采用了交叉校验法方法,并计算了以灰盒系统理论或灰盒识别的强度,以评价模型是否适合部分、不完整或不确定的数据。 关键流行病学参数最终被估算,我们为SEAIRIRD模型的构建提供了理由。当应用到巴西的案例时,SEAIRD为数据生成了一个极好的协议,以普通的R2-19美元(美元)和最低的ARIA_10美元数据序列。巴西的概率是100美元和90美元的平均传输。