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Register a new userCrowd Size using CommSense Instrument for COVID-19 Echo Period
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Added by
Santu Sardar
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The period after the COVID-19 wave is called the Echo-period. Estimation of crowd size in an outdoor environment is essential in the Echo-period. Making a simple and flexible working system for the same is the need of the hour. This article proposes and evaluates a non-intrusive, passive, and costeffective solution for crowd size estimation in an outdoor environment. We call the proposed system as LTE communication infrastructure based environment sensing or LTE-CommSense. This system does not need any active signal transmission as it uses LTE transmitted signal. So, this is a power-efficient, simple low footprint device. Importantly, the personal identity of the people in the crowd can not be obtained using this method. First, the system uses practical data to determine whether the outdoor environment is empty or not. If not, it tries to estimate the number of people occupying the near range locality. Performance evaluation with practical data confirms the feasibility of this proposed approach.
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The COVID-19 pandemic led to the adoption of severe measures to counteract the spread of the infection. Social distancing and lockdown measures modifies peoples habits, while the Internet gains a major role to support remote working, e-teaching, online collaboration, gaming, video streaming, etc. All these sudden changes put unprecedented stress on the network. In this paper we analyze the impact of the lockdown enforcement on the Politecnico di Torino campus network. Right after the school shutdown on the 25th of February, PoliTO deployed its own in-house solution for virtual teaching. Ever since, the university provides about 600 virtual classes daily, serving more than 16,000 students per day. Here, we report a picture of how the pandemic changed PoliTOs network traffic. We first focus on the usage of remote working and collaborative platforms. Given the peculiarity of PoliTO in-house online teaching solution, we drill down on it, characterizing both the audience and the network footprint. Overall, we present a snapshot of the abrupt changes on campus traffic and learning due to COVID-19, and testify how the Internet has proved robust to successfully cope with challenges and maintain the university operations.
Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. [...]
The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish different scenarios during the emergency, but their accuracy has been disappointing, forecasts ignore uncertainties and less attention is given to local areas. In this study, we propose a simple Multiple Linear Regression model, optimised to use call data to forecast the number of daily confirmed cases. Moreover, we produce a probabilistic forecast that allows decision makers to better deal with risk. Our proposed approach outperforms ARIMA, ETS and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures. The simplicity, interpretability and reliability of the model, obtained in a careful forecasting exercise, is a meaningful contribution to decision makers at local level who acutely need to organise resources in already strained health services. We hope that this model would serve as a building block of other forecasting efforts that on the one hand would help front-line personal and decision makers at local level, and on the other would facilitate the communication with other modelling efforts being made at the national level to improve the way we tackle this pandemic and other similar future challenges.
The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions COVID-19 dataset and the Allen Institute for AIs CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.
The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.
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