We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion $p_c$ per contact. The model uses known epidemiological parameters and the time dependent probability $p_c$ is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.

我们引入了具有年龄结构的 SARAS-CoV 病毒传播的分隔模型 SEIAHRV（易感、暴露、感染、无症状、住院、康复、接种疫苗）。为了模拟当前不同的疫苗，我们为接种了一剂和两剂疫苗且没有疫苗失败的个体使用隔室，为接种疫苗但疫苗失败的个体使用隔室。该模型允许考虑任何数量的不同疫苗，它们具有不同的功效和剂量之间的延迟。年龄组之间的接触由接触矩阵建模，传染矩阵是从传染概率中获得的$p_C$每个联系人。该模型使用已知的流行病学参数和时间相关概率$p_C$是通过将模型输出拟合到每个地区的一系列死亡而获得的，并反映了非药物干预。作为基准，将该模型的输出与两次高质量的血清学调查进行比较，并应用于研究 COVID-19 大流行在巴西总人口超过 100 万的主要城市的演变。我们还详细讨论了玛瑙斯市的案例，由于之前的一份报告而引起了特别关注自 COVID-19 大流行开始以来的各州。我们认为，此处提供的模型不仅与评估巴西的现行政策相关，而且与评估无法进行良好血清学调查的任何地方的现行政策相关。