The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible–Exposed–Infected–Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy: a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $\tau$ days. We also implement a random testing strategy with a threshold over the population. We found that $\tau =7$ is a good choice for the quarantine strategy since it reduces the infected population and, conveniently, it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, and the medical facilities to deal with the additional critical patients would be relatively low.

COVID-19 大流行给全球各级政府行政部门带来了挑战。当面对如此严重的疾病时，当局拥有预测工具来提前估计对卫生系统以及所需的人力、物力和经济资源的影响是有用的。在本文中，我们构建了一个扩展的易感-暴露-感染-恢复模型，其中纳入了马德普拉塔的社会结构，马德普拉塔是阿根廷第四大人口最多的城市，也是普埃雷东将军市政府的负责人。此外，我们根据疾病严重程度以及当地卫生资源的数据考虑感染者的详细分区，使预测更接近当地的实际情况。针对COVID-19调整相应的流行病参数，我们研究了一种交替隔离策略：一部分人群可以随时不受限制地流通，而其余人群则平均分为两组，连续进行正常活动和封锁，每个持续时间为$\tau$天。我们还实施了一种随机测试策略，其阈值超过了总体。我们发现$\tau =7$对于隔离策略来说是一个不错的选择，因为它可以减少感染人口，并且方便地适合每周的时间表。重点关注卫生系统，从截至 9 月 30 日的情况来看，鉴于所需的流动性极低，我们预计很难避免可用 ICU 饱和。在最坏的情况下，我们的模型估计会发生 4000 人死亡，其中 30% 通过适当的医疗护理是可以避免的。尽管如此，我们发现，积极的检测将使可以不受限制地流通的人数比例增加，而处理额外重症患者的医疗设施将相对较低。