A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

A real-time web tool for monitoring and mitigating indoor airborne COVID-19 transmission risks at city scale

Airborne transmission of aerosols contributes to a large portion of the SARS-CoV-2 spread indoors. This study develops a real-time interactive web-based platform for the public to compare various strategies to curb indoor airborne transmission of COVID-19 in different archetype buildings at a city scale. Although many countries have started vaccination and a gradual re-opening, because of emerging new variants of the virus and the possibility of future pandemics, a lively updated tool for monitoring and mitigation of infection risk is essential. As a demonstration, we evaluated the impacts of six mitigation measures on the infection risks in various building types in a city. It shows that the same strategy could perform quite differently, depending on building types and properties. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the minimum required outdoor air ventilation rate is not as effective as other strategies to reduce the risk. It also causes considerable penalties on energy consumption. Therefore, new building ventilation standards, control actions, and design criteria should be considered to mitigate the infection risk and save energy.

Exploring the nexus between social vulnerability, built environment, and the prevalence of COVID-19: A case study of Chicago

COVID-19 has significantly and unevenly impacted the United States, disproportionately affecting socially vulnerable communities. While epidemiologists and public health officials have suggested social distancing and shelter-in-place orders to halt the spread of this virus, the ability to comply with these guidelines is dependent on neighborhood, household, and individual characteristics related to social vulnerability. We use structural equation modeling and multiple data sources, including anonymized mobile phone location data from SafeGraph, to examine the effects of different social vulnerability and built environment factors on COVID-19 prevalence over two overlapping time periods (March to May and March to November of 2020). We use Chicago, Illinois as a case study and find that zip codes with low educational attainment consistently experienced higher case rates over both periods. Though population density was not significantly related to the prevalence in any period, movement of people made a significant contribution only during the longer time period. This finding highlights the significance of analyzing different timeframes for understanding social vulnerability. Our results suggest social vulnerability played an influential role in COVID-19 prevalence, highlighting the needs to address socioeconomic barriers to pandemic recovery and future pandemic response.

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Digital contact tracing provides an expeditious and comprehensive way to collect and analyze data on people's proximity, location, movement, and health status. However, this technique raises concerns about data privacy and its overall effectiveness. This paper contributes to this debate as it provides a systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021. Following the PRISMA protocol for systematic reviews and the CHEERS statement for quality assessment, 580 papers were initially screened, and 19 papers were included in a qualitative synthesis. We add to the current literature in three ways. First, we evaluate whether digital contact tracing can mitigate COVID-19 by either reducing the effective reproductive number or the infected cases. Second, we study whether digital is more effective than manual contact tracing. Third, we analyze how proximity/location awareness technologies affect data privacy and population participation. We also discuss proximity/location accuracy problems arising when these technologies are applied in different built environments (i.e., home, transport, mall, park). This review provides a strong rationale for using digital contact tracing under specific requirements. Outcomes may inform current digital contact tracing implementation efforts worldwide regarding the potential benefits, technical limitations, and trade-offs between effectiveness and privacy.

What determines city's resilience against epidemic outbreak: evidence from China's COVID-19 experience

By employing the city-level data from China during the spring of 2020, this study investigates the relationship between city-level resilience against the outbreak of COVID-19 pandemics and its affecting factors, including the inflow risk pressure of COVID-19 virus (population inflow from the epicenter), city agglomeration characteristics (urban population density and city size), healthcare resource adequacy, among others. The results reveal that, while managing COVID-19 inflow risk pressure plays a critical role in the city's pandemic disaster resilience, city agglomeration characteristics also matters. To be exact, we find that large and high-density cities with high inter and intra-city mobility flows have more difficulties in containing the epidemic spread, but improving healthcare infrastructure adequacy and urban governance capacity can increase time efficacy of pandemic control and then improve the city's resilience against pandemic. Although our analysis is based on the performance of Chinese cities in the case of COVID-19, the research framework can be applied in understanding COVID-19 control performance of cities in other countries and the findings can be useful for improving health-related urban resilience and sustainability.

Leveraging artificial intelligence to analyze the COVID-19 distribution pattern based on socio-economic determinants

The spatialization of socioeconomic data can be used and integrated with other sources of information to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics of city dwellers and their spatial and temporal variability. This work focuses on such applications to explore the underlying association between socioeconomic characteristics of different geographical regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in electoral divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied.

ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence

Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject's infection, backpropagation through the subjects' location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects.

A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach

Social distancing in public spaces plays a crucial role in controlling or slowing down the spread of coronavirus during the COVID-19 pandemic. Visual Social Distancing (VSD) offers an opportunity for real-time measuring and analysing the physical distance between pedestrians using surveillance videos in public spaces. It potentially provides new evidence for implementing effective prevention measures of the pandemic. The existing VSD methods developed in the literature are primarily based on frame-by-frame pedestrian detection, addressing the VSD problem from a static and local perspective. In this paper, we propose a new online multi-pedestrian tracking approach for spatio-temporal trajectory and its application to multi-scale social distancing measuring and analysis. Firstly, an online multi-pedestrian tracking method is proposed to obtain the trajectories of pedestrians in public spaces, based on hierarchical data association. Then, a new VSD method based on spatio-temporal trajectories is proposed. The proposed method not only considers the Euclidean distance between tracking objects frame-by-frame but also takes into account the discrete Fréchet distance between trajectories, hence forms a comprehensive solution from both static and dynamic, local and holistic perspectives. We evaluated the performance of the proposed tracking method using the public dataset MOT16 benchmark. We also collected our own pedestrian dataset "SCU-VSD" and designed a multi-scale VSD analysis scheme for benchmarking the performance of the social distancing monitoring in the crowd. Experiments have demonstrated that the proposed method achieved outstanding performance on the analysis of social distancing.