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Register a new userData analysis on Coronavirus spreading by macroscopic growth laws
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To evaluate the effectiveness of the containment on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in a selected number of different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law. We also propose here a generalization of Gompertz law. Our data analysis permits an evaluation of the maximum number of infected individuals. The daily data must be compared with the obtained fits, to verify if the spreading is under control. From our analysis it appears that the spreading reached saturation in China, due to the strong containment policy of the national government. In Singapore a large growth rate, recently observed, suggests the start of a new strong spreading. For South Korea and Italy, instead, the next data on new infections will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals.
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Here, we focus on the data analysis of the growth of epidemic spread of Covid-19 in countries where different policies of containment were activated. It is known that the growth of pandemic spread at its threshold is exponential, but it is not known how to quantify the success of different containment policies. We identify that a successful approach gives an arrested phase regime following the Ostwald growth, where, over the course of time, one phase transforms into another metastable phase with a similar free energy as observed in oxygen interstitial diffusion in quantum complex matter and in crystallization of proteins. We introduce the s factor which provides a quantitative measure of the efficiency and speed of the adopted containment policy, which is very helpful not only to monitor the Covid-19 pandemic spread but also for other countries to choose the best containment policy. The results show that a policy based on joint confinement, targeted tests, and tracking positive cases is the most rapid pandemic containment policy; in fact, we found values of 9, 5, and 31 for the success s factor for China, South Korea, and Italy, respectively, where the lowest s factor indicates the best containment policy
We show that qualitatively different epidemic-like processes from distinct societal domains (finance, social and commercial blockbusters, epidemiology) can be quantitatively understood using the same unifying conceptual framework taking into account the interplay between the timescales of the grouping and fragmentation of social groups together with typical epidemic transmission processes. Different domain-specific empirical infection profiles, featuring multiple resurgences and abnormal decay times, are reproduced simply by varying the timescales for group formation and individual transmission. Our model emphasizes the need to account for the dynamic evolution of multi-connected networks. Our results reveal a new minimally-invasive dynamical method for controlling such outbreaks, help fill a gap in existing epidemiological theory, and offer a new understanding of complex system response functions.
The 3D fundamental diagrams and phase portraits for tunnel traffic is constructed based on the empirical data collected during the last years in the deep long branch of the Lefortovo tunnel located on the 3rd circular highway in Moscow. This tunnel of length 3 km is equipped with a dense system of stationary ra-diodetetors distributed uniformly along it chequerwise at spacing of 60 m. The data were averaged over 30 s. Each detector measures three characteristics of the vehicle ensemble; the flow rate, the car velocity, and the occupancy for three lanes individually. The conducted analysis reveals complexity of phase states of tunnel traffic. In particular, we show the presence of cooperative traffic dynamics in this tunnel and the variety of phase states different in properties. Besides, the regions of regular and stochastic dynamics are found and the presence of dynamical traps is demonstrated.
We have recently introduced the ``thermal optimal path (TOP) method to investigate the real-time lead-lag structure between two time series. The TOP method consists in searching for a robust noise-averaged optimal path of the distance matrix along which the two time series have the greatest similarity. Here, we generalize the TOP method by introducing a more general definition of distance which takes into account possible regime shifts between positive and negative correlations. This generalization to track possible changes of correlation signs is able to identify possible transitions from one convention (or consensus) to another. Numerical simulations on synthetic time series verify that the new TOP method performs as expected even in the presence of substantial noise. We then apply it to investigate changes of convention in the dependence structure between the historical volatilities of the USA inflation rate and economic growth rate. Several measures show that the new TOP method significantly outperforms standard cross-correlation methods.
Gamma-ray spectral data were collected from sensors mounted to traffic signals around Northern Virginia. The data were collected over a span of approximately fifteen months. A subset of the data were analyzed manually and subsequently used to train machine-learning models to facilitate the evaluation of the remaining 50k anomalous events identified in the dataset. We describe the analysis approach used here and discuss the results in terms of radioisotope classes and frequency patterns over day-of-week and time-of-day spans. Data from this work has been archived and is available for future and ongoing research applications.
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