论文标题
通过A-SIR模型预测Covid-19的流行病的演变：伦巴第，意大利和圣保罗州，巴西
Predicting the evolution of the COVID-19 epidemic with the A-SIR model: Lombardy, Italy and São Paulo state, Brazil
论文作者
论文摘要
症状很少或没有症状的大量感染者的存在是重要的流行病学困难，也是Covid-19的主要数学特征。 A-SIR模型，即带有一个没有症状或症状很少的感染者的SIR（易感性感染的）模型，Arxiv：2003.08720 [q-bio.pe]（2020）提出了A-SIR模型。在本文中，我们研究了同一模型的略有概括性版本，并提出了一种将模型参数拟合到仅使用已故个体的时间序列的计划。该方案适用于意大利伦巴第和巴西圣保罗州的具体案例，显示了流行病的不同方面。对于每种情况，我们都表明，直到现在，我们可能与数据具有良好的适合，但是将来的行为差异很大。这种不同结果背后的原因是关键参数的价值的不确定性，感染者完全有症状的可能性以及采用的社会疏远措施的强度。这一结论强制了试图确定人群中受感染者的实际数量，有症状或无症状的人数。
The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Giuseppe Gaeta, arXiv:2003.08720 [q-bio.PE] (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and São Paulo state, Brazil, showing different aspects of the epidemics. For each case we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic.
