Buscema Paolo Massimo, Della Torre Francesca, Breda Marco, Massini Giulia, Grossi Enzo
Semeion Research Center of Sciences of Communication, via Sersale, 117, 00128 Rome, Italy.
University of Colorado at Denver, Department of Mathematical and Statistical Sciences, Denver, CO, USA.
Physica A. 2020 Nov 1;557:124991. doi: 10.1016/j.physa.2020.124991. Epub 2020 Jul 25.
In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.
在本文中，我们想展示一种名为拓扑加权质心（TWC）的进化算法的潜力。该算法能够从极其有限且质量不佳的数据集中获取新的相关信息。在这个由大数据概念主导的世界里，我们想表明，无论出于必要还是选择，即使处理少量数据也有可能实现有效运作。该算法的这一特性意味着，即使在疫情初期，数据少到无法进行充分统计时，也有可能获取重要信息。为了证明我们的理论，我们探讨了当下最核心的问题之一：新冠疫情。具体而言，我们选取了意大利记录的病例。由于检测到的感染人数众多，意大利在这场疫情中似乎扮演着核心角色。通过这种创新的人工智能算法，我们尝试利用一个仅包含首批记录病例地理空间坐标（经度和纬度）的数据集来分析这一现象的演变，并预测其未来发展。一旦收集到截至2020年2月26日至少有一例传染病例被官方确诊地点的坐标，我们就开展了以下研究与分析：爆发点及相关热图（TWC阿尔法）；2月26日传染的概率分布（TWC贝塔）；该现象在近期以及更长远未来可能的传播情况（TWC伽马和TWC西塔）；这种传播在路径和相互影响方面是如何发生的（西塔路径和马尔可夫机器）。最后，绘制了一张关于疫情结束时各地区传染性可能情况的热图。使用TWC进行的分析证实了一开始所做的假设。
