Fioriti Vincenzo, Chinnici Marta, Arbore Andrea, Sigismondi Nicola, Roselli Ivan
ENEA- C.R Casaccia, Via Anguillarese 301, Rome, 00123, Italy.
ICT-Technical Consultant, Rome, Italy.
Heliyon. 2021 Nov;7(11):e08422. doi: 10.1016/j.heliyon.2021.e08422. Epub 2021 Nov 18.
Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified.
了解传染病爆发的早期增长情况对于估计疫情传播至关重要。为此已经开发了大量数学工具，以应对大量不同的动态演变，范围从亚线性增长到超指数增长。当然，关键在于在疫情初期我们没有足够的数据来进行可靠的推断。在此，我们提出一种直接的方法，只要整个地区有监测系统，就可以根据仅一周的累计感染数据来估计疫情增长动态。该方法基于纽科姆 - 本福特定律，应用于意大利新冠肺炎案例研究。结果表明，利用在意大利五十个城市收集的前七个数据点能够区分疫情动态。此外，还确定了六周疫情情景内最可能的增长近似函数。
