Contact Tracing over Uncertain Indoor Positioning Data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE

Changes in adult trauma at a level 1 trauma center before and during the COVID-19 pandemic

Background Prior research has shown the COVID-19 pandemic is associated with changes in ED volumes, trauma caseloads and distribution of disease. Objectives We aim to characterize the impact of the COVID-19 pandemic at a diverse, high-volume Level 1 trauma center in the US. Methods We performed a retrospective review of our institutional trauma registry at our center from 2018 through 2021 to study changes before and after COVID-19. We established March 14 – December 31 as the study period of interest for each year. We analyzed the data with descriptive statistics and created Poisson regression models to determine the estimated percentage year-to-year changes. Results Total number of trauma cases increased with each subsequent year from 2018 (N = 4605) to 2021 (N = 7331) (total N = 23,727). In general, the proportion of Black or African American patients increased over time (2018: 19.2%, 2021: 23.0%). The proportion of patients insured by Medicaid (8.0% vs 10.5%) and Medicare (26.5% vs 32.8%) increased from 2018 to 2021. Comparing 2019 to 2020, we found increases in violent traumas: GSW (+88.6%, 95% CI 63.8%–117.2%) and stabbings (+39.6%, 95% CI 8.1%–80.3%). Trauma patient ED LOS decreased from 300 min (67–400 IQR) in 2018 to 249 min in 2021 (104–510 IQR). Conclusion This analysis identified increased trauma volumes, especially violent trauma (GSW, stabbing, other penetrating). There was a greater proportion of Black/African American patients and those insured with Medicare or Medicaid during the pandemic. TED LOS decreased over time while ED mortality and hospital LOS remained stable.

Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry

The COVID-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. However, the specific impact of COVID-19 on retailers at the customer end has not yet been addressed. Using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading UK supermarkets during the first COVID-19 lockdown. The results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets' response actions. Drawing on the social media crisis communication framework and Situational Crisis Communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. The results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. As new variants of COVID-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. Based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the UK grocery sector in the context of COVID-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. This study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. It also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies. © 2022 The Authors

Continuous Social Distance Monitoring in Indoor Space

The COVID-19 pandemic has caused over 6 million deaths since 2020. To contain the spread of the virus, social distancing is one of the most simple yet effective approaches. Motivated by this, in this paper we study the problem of continuous social distance monitoring (SDM) in indoor space, in which we can monitor and predict the pairwise distances between moving objects (people) in a building in real time. SDM can also serve as the fundamental service for downstream applications, e.g., a mobile alert application that prevents its users from potential close contact with others. To facilitate the monitoring process, we propose a framework that takes the current and future uncertain locations of the objects into account, and finds the object pairs that are close to each other in a near future. We develop efficient algorithms to update the result when object locations update. We carry out experiments on both real and synthetic datasets. The results verify the efficiency and effectiveness of our proposed framework and algorithms. © 2022, American Mathematical Society. All rights reserved.

Policy-oriented restart of supply and demand after COVID-19: firm-level evidence for China

Purpose China is the only major economy in the world that has achieved positive gross domestic product (GDP) growth in 2020. The paper aims to explore the effect of China's public policy restarting supply and consumption after coronavirus disease-2019 (COVID-19). Design/methodology/approach Affected by the epidemic, global economic growth slowed down. Using the stock price data of Chinese A-share listed company, combining natural experiment and event study method, the paper examines the policy effects of work resumption and consumer vouchers. Findings Compared with demand capacity, the work resumption has a more significant role in promoting the supply industry. Issuing consumer vouchers can effectively promote local demand recovery, and the effect is mainly concentrated in the industries involved in consumption vouchers. At the same time, public management capacity and the income level of residents play an important role in restarting supply and demand. Practical implications Understanding China's public policies and effects are of positive significance to the restoration of economic development in other countries. Originality/value The study contributes to knowledge by empirically examining the effect of China's public policies against the COVID-19 pandemic. The paper also expands the scope of policy-oriented research based on the perspective of supply and demand capacity building.