Estimating the epidemic growth dynamics within the first week. Issue 11 (November 2021)

• Record Type: Journal Article
• Authors: Fioriti, Vincenzo; Chinnici, Marta; Arbore, Andrea; Sigismondi, Nicola; Roselli, Ivan
• Is Part Of: Heliyon. Volume 7:Issue 11(2021)
• Publication Date: 2021-11
• Subjects: Complex network -- Dynamical systems -- Graph theory -- Big data -- Epidemic spreading -- Infective diseases; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2021.e08422
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: 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. Abstract : Complex network, Dynamical systems, Graph theory, Big data, Epidemic spreading, Infective diseases.