论文标题
在随机模型中检测被感染的无症状病例，以进行共vid-19的传播。阿根廷的情况
Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19. The case of Argentina
论文作者
论文摘要
我们研究了阿根廷Covid-19的动态演变。人口密度的明显异质性和该国非常广泛的地理作用成为挑战本身。标准隔室模型在阿根廷案件中实施时失败。我们扩大了以前的成功模型，以两种基本方式描述了2009年AH1N1流感流行病的地理扩散：我们添加了一种随机的局部移动机制，我们引入了一个新的隔间，以考虑到被感染的无症状检测到的人的隔离。两个基本参数驱动了动态：感染者传染性和隔离之间的时间（$α$）与被感染总数的人的比率（$α$）（$ p $）。进化对$ p-$参数更敏感。该模型不仅重现了真实数据，而且还可以预测前者消失之前的第二波。这种影响是具有异质种群密度和相互联系的广泛国家的内在效果。此处介绍的模型成为公共政策影响的良好预测指标，例如，目前从世界上开始的不可避免的疫苗接种运动。
We have studied the dynamic evolution of the Covid-19 pandemic in Argentina. The marked heterogeneity in population density and the very extensive geography of the country becomes a challenge itself. Standard compartment models fail when they are implemented in the Argentina case. We extended a previous successful model to describe the geographical spread of the AH1N1 influenza epidemic of 2009 in two essential ways: we added a stochastic local mobility mechanism, and we introduced a new compartment in order to take into account the isolation of infected asymptomatic detected people. Two fundamental parameters drive the dynamics: the time elapsed between contagious and isolation of infected individuals ($α$) and the ratio of people isolated over the total infected ones ($p$). The evolution is more sensitive to the $p-$parameter. The model not only reproduces the real data but also predicts the second wave before the former vanishes. This effect is intrinsic of extensive countries with heterogeneous population density and interconnection. The model presented here becomes a good predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world.
