The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

The declaration of the COVID-19 pandemic and the resulting lockdowns brought focus on the importance of the retail sector for community well-being. The restrictive government policies that were put into place to curb the spread of COVID-19 virus added pressure on retailers to adapt to the subsequent changes in consumption. This research, using a case study of Erie County in the State of New York (NY), investigates these changes in visitation patterns for a commercial service sector that was deemed 'essential' - food and beverage. This study uses mobile location data to identify variations in shopping patterns for independent and chain stores. The study finds that by comparing the pre-pandemic to pandemic, there were changes to visitation patterns over time and between retail types. While the study highlights the potential to use mobile data to study shifts in consumption behaviours, the paper also reveals several challenges in using such data. © 2022 IEEE Computer Society. All rights reserved.

Using Mobile Data for Understanding Population Movement and Disease Transmission during Covid-19 Outbreak in the Nordics

This study investigates the use of mobile data to understand patterns of population movements and disease transmission during the Covid-19 outbreak. It also focuses on understanding the implications of using this data for individual privacy. Using a mixed methods approach, we present 10 rich qualitative interviews and 412 survey responses from participants across the Nordics. Our novel results show that the use of mobile data can be characterized by two main categories: validation data and complementary data. We also identify five implications for practice: sharing resources and expertise between health agencies and telecom companies;extended collaboration with multiple network operators;cross-disciplinary collaboration among multiple parties;developing data and privacy guidelines;and developing novel methods and tools to address the trade-off between maintaining individual privacy and obtaining detailed information from mobile data. These implications may inform immediate and future actions to prepare for, mitigate, and control the spread of infectious diseases using mobile data. They also show privacy-driven limitations of mobile data in terms of data accuracy, richness, and scope. © 2022 IEEE Computer Society. All rights reserved.

Assessing pandemic era stadium events and infections using mobile phone based population mobility data: An exploratory study from Ireland, 2021

Mass gathering events (MGEs) attracting local, national, or international crowds presented particular challenges in the context of the coronavirus disease 2019 (COVID-19) pandemic. Sporting, religious, music and other cultural events held during the early months of the pandemic, without social distancing or other safeguards, have been regarded as so-called ‘super spreader’ events. By the summer of 2020, MGEs were generally banned or subject to severe restrictions. Regular European sporting fixtures such as England’s Football Association and Germany’s Bundesliga matches began to return in the autumn with protective measures in place, such as matches initially held behind closed doors, and later with sub-capacity crowd limits and mandatory social distancing [1, 2, 3, 4, 5].With protective measures in place, and proof of COVID-19 vaccination or recovery required for entry, a series of six sporting MGEs, ‘the All-Ireland Finals’ were held in the Republic of Ireland’s largest stadium, Croke Park in Dublin, during August-September 2021. This study draws on a high-resolution human population mobility dataset to quantify journeys to/from the stadium area on MGE days by destination. The anonymised, aggregated, data used is based on mobile phone usage, and consists of a series of fine-grained geographical origin-destination matrices presenting daily estimates of area to area journey numbers. With mobility from the stadium area serving as a proxy for MGE attendance, this study explores associations between MGE attendance numbers and local COVID-19 infections over subsequent five week periods. No evidence was found of association between attendance at any of the six 2021 All-Ireland MGEs and COVID-19 infections over subsequent five week periods. This finding contrasts with studies of comparable MGEs in 2020, such as English Association Football matches held during spring 2020, and German Bundesliga football matches held during autumn 2020. These differing outcomes may point to the effectiveness of transmission mitigation policies and behaviours. [ FROM AUTHOR]

The Remote Emerging Disease Intelligence-NETwork.

Accurate prediction of zoonotic spillover events requires a detailed understanding of baseline pathogens circulating in differing global environments. By characterizing the diversity and determining the natural baseline of pathogens in a given biological system, any perturbations to this balance can be detected, leading to estimates of risk for emerging diseases. As epidemics and probability for pandemics increase, there is a fundamental need for building global collaborations to fill gaps in the surveillance effort, especially to build remote in-county capacity and standardize timely sample processing and data analysis. To this point, a new consortium, the Remote Emerging Disease Intelligence-NETwork (REDI-NET) has been established to enhance surveillance approaches and characterize natural pathogens in temperate, tropical forest, and tropical grassland biomes. The REDI-NET is envisioned to be a long-term, phased initiative. All phases will integrate accompanying training resources such as videos reflecting SOPs and Quick Reference Guides. Routine bio- and xenosurveillance will facilitate the characterization of ecological parameters, enhance the accuracy of vector species identification using artificial intelligence technology, and guide the establishment of epidemiological risk thresholds critical for mitigating disease outbreaks in a timely manner. A key deliverable of the REDI-NET is a custom-designed electronically merged (e-MERGE) data pipeline and alert dashboard that integrates remotely captured data with state-of-the-art metagenomic next-generation sequencing technology. This pipeline incorporates data generated from field and laboratory best practices, to furnish health decision-makers with a centralized, timely, and rigorous database to efficiently search interdisciplinary and heterogeneous data sources necessary to alert, prepare and mitigate health threats. The e-MERGE pipeline, once fully established, will be a flexible, scalable, and expandable tool for varied health applications. Program success will result in an operational framework that addresses resource gaps in pathogen surveillance and enhances health protection with broad global applicability. The objective of this manuscript is to introduce the REDI-NET framework to anticipated stakeholders engaged in metagenomics, epidemiological surveillance, and One Health with a focus on Phase 1.

TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks

As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users' preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts. © 2022 IEEE.

Population Mobility Patterns and Monitoring of COVID-19 Restriction Measures in Latvia

Compared to the spring, when the Covid-19 pandemic started and people honestly followed the precautionary measures, the behavior of the Latvian population has changed significantly. The majority of Latvians do not exercise caution, and their activity has returned to pre-Covid-19 levels this autumn, negatively affecting the epidemiological situation in the country, according to an analysis of population behavior. Within the research, the epidemiological statistics of Center for Disease Prevention and Control and Latvian Mobile Telephone (LMT) mobile network events were analyzed to determine the relationship between population activity and epidemiological situation in Latvia as a whole, as well as in each region. According to the performed analysis, it is possible to divide Latvia into two parts - municipalities that were active during the emergency situation and places where the greatest activity is observed before and after the emergency situation. It was concluded that mobile call activity during emergencies in both cities and counties is still high, it is 70% - 80% of the precrisis period. Since the spring, people's behavior and habits have changed significantly, so a different approach is needed.

Estimating the COVID-19 Spread Through Real-time Population Mobility Patterns: Surveillance in Low- and Middle-Income Countries.

BACKGROUND: On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries. OBJECTIVE: The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space-time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide. METHODS: We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space-time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used. RESULTS: We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread. CONCLUSIONS: The prospective space-time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.