Making Chile More Pedestrian-Friendly for Older Persons: Expert Perspectives.

Walking provides numerous benefits to older persons, but its practice is hindered by social factors and the built environment. This article aims to understand the factors that encourage or discourage older people's walking behaviors, as well as the policies influencing those factors in Chile. It does so by reporting the analysis of twenty-five semi-structured interviews with Chilean policymakers and local leaders. The experts consistently represented walking as a beneficial activity for older persons that, nonetheless, occurs in adverse built environments. They asserted that the absence of older people in the public discussion and a top-down policy-making structure hamper its promotion.

External Human-Machine Interfaces for Automated Vehicles in Shared Spaces: A Review of the Human-Computer Interaction Literature.

Given the rise of automated vehicles from an engineering and technical perspective, there has been increased research interest concerning the Human and Computer Interactions (HCI) between vulnerable road users (VRUs, such as cyclists and pedestrians) and automated vehicles. As with all HCI challenges, clear communication and a common understanding-in this application of shared road usage-is critical in order to reduce conflicts and crashes between the VRUs and automated vehicles. In an effort to solve this communication challenge, various external human-machine interface (eHMI) solutions have been developed and tested across the world. This paper presents a timely critical review of the literature on the communication between automated vehicles and VRUs in shared spaces. Recent developments will be explored and studies analyzing their effectiveness will be presented, including the innovative use of Virtual Reality (VR) for user assessments. This paper provides insight into several gaps in the eHMI literature and directions for future research, including the need to further research eHMI effects on cyclists, investigate the negative effects of eHMIs, and address the technical challenges of eHMI implementation. Furthermore, it has been underlined that there is a lack of research into the use of eHMIs in shared spaces, where the communication and interaction needs differ from conventional roads.

Identification of social groups and waiting pedestrians at railway platforms using trajectory data.

To investigate the impact of social groups on waiting behaviour of passengers at railway platforms a method to identify social groups through the monitoring of distances between pedestrians and the stability of those distances over time is introduced. The method allows the recognition of groups using trajectories only and thus opens up the possibility of studying crowds in public places without constrains caused by privacy protection issues. Trajectories from a railway platform in Switzerland were used to analyse the waiting behaviour of passengers in dependence of waiting time as well as the size of social groups. The analysis of the trajectories shows that the portion of passengers travelling in groups reaches up to 10% during the week and increases to 20% on the weekends. 60% of the groups were pairs, larger groups were less frequent. With increasing group size, the mean speed of the members decreases. Individuals and pairs often choose waiting spots at the sides of the stairs and in vicinity of obstacles, while larger groups wait close to the platform entries. The results indicate that passengers choose waiting places according to the following criteria and ranking: shortest ways, direction of the next intended action, undisturbed places and ensured communication. While individual passengers often wait in places where they are undisturbed and do not hinder others, the dominating comfort criterion for groups is to ensure communication. The results regarding space requirements of waiting passengers could be used for different applications. E.g. to enhance the level of service concept assessing the comfort of different types of users, to avoid temporary bottlenecks to improve the boarding and alighting process or to increase the robustness of the performance of railway platforms during peak loads by optimising the pedestrian distribution.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

Socioeconomic correlates of face mask use among pedestrians during the COVID-19 pandemic: An ecological study.

Background: Many countries have recommended using face masks for the general population in public places to reduce the risk of COVID-19 transmission. This study aimed to assess the effects of socioeconomic status on face mask use among pedestrians during the COVID-19 pandemic. Methods: This cross-sectional study was conducted in Ahvaz, southwest Iran in August 2020. A total of 10,440 pedestrians have been studied from 92 neighborhoods of the city. Three socioeconomic indicators including Land price, Literacy rate, and the Employment rate for each neighborhood were used in this study. Analysis of Covariance and partial correlation coefficients were applied to assess the relationship between prevalence rates of mask usage and SES indicators. Results: The mean ± SD age of the pedestrians was 32.2 ± 15.1 years. Of 10,440 observed participants, 67.9% were male. The overall prevalence of face mask usage was 45.6%. The prevalence of mask usage in older people and women was significantly higher than the others. The three assessed socioeconomic indicators were directly correlated to the prevalence of mask usage at individual and neighborhood levels. Conclusion: We found that literacy had the strongest correlation with the prevalence of mask usage compared to the land price and employment rate among the three assessed SES indicators. Hence, it can be concluded that the social component of socioeconomic status has a greater effect on mask usage by people than the economic component of socioeconomic status.

Frequency of Correct Mask Use Among Pedestrians in High-Flow Indoor Public Spaces: A Multicenter Analysis in the First and Second Waves of the COVID-19 Pandemic in Peru.

BACKGROUND: Our study aimed to describe the variation in the frequency of correct mask use among pedestrians in the first and second waves of the COVID-19 pandemic in high-flow indoor public spaces from different geographic and social settings in Peru. METHODS: We carried out a cross-sectional exploratory study among pedestrians in Lima (the capital city) and other coastal and highland cities in Peru. Pedestrians were directly observed by trained medical students in 2 high-flow indoor areas at different times in November 2020 (first wave) and October 2021 (second wave). Primary outcomes included the frequencies of mask use and correct use. We applied multinomial logistic models and estimated crude and adjusted relative prevalence ratios for sex, age, obesity, and location. Additionally, we used binomial generalized linear models to estimate prevalence ratios in crude and adjusted models. RESULTS: We included 1996 participants. The frequency of mask use was similar in both years: 96.9% in 2020 and 95.5% in 2021. However, the frequency of correct mask use significantly decreased from 81.9% (95% CI, 79.4-84.3) in 2020 to 60.3% (95% CI, 57.2-67.3) in 2021. In 2020, we observed an increase in the probability of misuse in the cities of Lima (aRP: 1.42; P = .021) and Chiclayo (aPR: 1.62, P = .001), whereas, in 2021, we noted an increase in the probability of misuse in the cities of Lima (aRP: 1.72; P < .001) and Piura (aPR: 1.44; P < .001). CONCLUSIONS: The correct mask use decreased during the second wave, although no significant overall variations were observed in mask use in pedestrians between both periods. Also, we found regional differences in correct mask use in both periods.

A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus.

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value.

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion.

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.

PERSIAN Traffic Safety and Health Cohort: a study protocol on postcrash mental and physical health consequences.

BACKGROUND: Cohort studies play essential roles in assessing causality, appropriate interventions. The study, Post-crash Prospective Epidemiological Research Studies in IrAN Traffic Safety and Health Cohort, aims to investigate the common health consequences of road traffic injuries (RTIs) postcrash through multiple follow-ups. METHODS: This protocol study was designed to analyse human, vehicle and environmental factors as exposures relating to postcrash outcomes (injury, disability, death, property damage, quality of life, etc). Population sources include registered injured people and followed up healthy people in precrash cohort experienced RTIs. It includes four first-year follow-ups, 1 month (phone-based), 3 months (in-person, video/phone call), 6 and 12 months (phone-based) after crash. Then, 24-month and 36-month follow-ups will be conducted triennially. Various questionnaires such as Post-traumatic Stress Disorder Questionnaire, Patient Health Questionnaire, WHO Disability Assessment Schedules, Cost-related Information, etc are completed. Counselling with a psychiatrist and a medical visit by a practitioner are provided accompanied by extra tools (simulator-based driving assessment, and psychophysiological tests). Through preliminary recruitment plan, 5807, 2905, 2247 and 1051 subjects have been enrolled, respectively at the baseline, first, second and third follow-ups by now. At baseline, cars and motorcycles accounted for over 30% and 25% of RTIs. At first follow-up, 27% of participants were pedestrians engaged mostly in car crashes. Around a fourth of injuries were single injuries. Car occupants were injured in 40% of collisions. DISCUSSION: The study provides an opportunity to investigate physical-psychosocial outcomes of RTIs, predictors and patterns at follow-up phases postinjury through longitudinal assessments, to provide advocates for evidence-based safety national policy-making.

Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic.

Introduction: To combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population's behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device. Objective: This work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions. Methods: Spatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed. Results: The sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower. Conclusions: The study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study's core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

The Utility of DSRC and V2X in Road Safety Applications and Intelligent Parking: Similarities, Differences, and the Future of Vehicular Communication.

As the technological advancement in the automotive field increases and the complexity of vehicle and infrastructure applications is extremely high, new directions and approaches are needed in this field. Supporting and developing vehicular applications dedicated to road safety by analyzing the current behavior of existing networks in various forms is imperative. This paper studies and implements a DSRC-type communications infrastructure that receives a set of controllable and adjustable indicators, which can provide messages to network drivers in a timely manner. The implementation is based on the 802.11p protocol and initially addresses pedestrian infrastructure or pedestrian safety, controlled areas, and perimeters that allow intelligent communications. The design and setting of the communication parameters in the lower layer of the DSRC stack for vehicle applications are part of this work, aspects that are also relevant in the case of autonomous vehicles.

Applying a Pedestrian Level of Service in the Context of Social Distancing: The Case of the City of Madrid.

During the COVID-19 pandemic, there has been a lot of discussion about keeping interpersonal distance to prevent the virus from spreading. To keep this interpersonal distance, authorities at different levels have taken measures to reduce people's interactions, such as reducing capacities, curfews, pop-up cycle lanes, temporary pedestrianisation, and lockdowns. Many of these temporary measures have been perceived from a static view. Nevertheless, in a scenario of "new normality" or in the face of a possible new pandemic, the amount of data (big data) generated by different sources, such as sensors, in large cities has extraordinary potential to be used together with tactical urbanism for quick adaptation. The aim of this study was to gain insight into the aforementioned issues by analysing spatio-temporal patterns of pedestrian mobility and developing a variation of the pedestrian level of service measure; the pandemic pedestrian level of service (P-PLOS). This measure provides a dynamic view of pavement capacities according to the interpersonal distance recommendations during the pandemic. P-PLOS was tested in the city of Madrid based on the pedestrian counter data that was provided by the local government through its open data website. We found that the application of P-PLOS, together with street design, allows for knowing where and when it is necessary to take tactical urbanism measures in order to maintain or improve the level of service, as well as where it is necessary to take measures to reduce pedestrian flow.

Monocular Pedestrian 3D Localization for Social Distance Monitoring.

Social distancing protocols have been highly recommended by the World Health Organization (WHO) to curb the spread of COVID-19. However, one major challenge to enforcing social distancing in public areas is how to perceive people in three dimensions. This paper proposes an innovative pedestrian 3D localization method using monocular images combined with terrestrial point clouds. In the proposed approach, camera calibration is achieved based on the correspondences between 2D image points and 3D world points. The vertical coordinates of the ground plane where pedestrians stand are extracted from the point clouds. Then, using the assumption that the pedestrian is always perpendicular to the ground, the 3D coordinates of the pedestrian's feet and head are calculated iteratively using collinear equations. This allows the three-dimensional localization and height determination of pedestrians using monocular cameras, which are widely distributed in many major cities. The performance of the proposed method was evaluated using two different datasets. Experimental results show that the pedestrian localization error of the proposed approach was less than one meter within tens of meters and performed better than other localization techniques. The proposed approach uses simple and efficient calculations, obtains accurate location, and can be used to implement social distancing rules. Moreover, since the proposed approach also generates accurate height values, exclusionary schemes to social distancing protocols, particularly the parent-child exemption, can be introduced in the framework.

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras.

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the 'busyness' of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK's Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).

Mask use among pedestrians during the Covid-19 pandemic in Southwest Iran: an observational study on 10,440 people.

BACKGROUND: Many countries have recommended the use of face masks for general population in public places to reduce the risk of COVID-19 transmission. This study aims to estimate the prevalence of face mask usage and investigate about different types of face mask and their distribution among pedestrians in southwest Iran during the Covid-19 pandemic. METHODS: This cross-sectional study was conducted in August 2020 in Ahvaz, southwest Iran. Using a multistage sampling method, a total of 10,440 pedestrians selected from 8 urban districts and 92 neighborhoods of the city. The data gathered by observation method. Percentage, mean and standard deviation were used to describe the variables. Chi-square test, fisher exact test and Chi-square for trend used to assess relationship between two categorical variables. We used unconditional logistic regression model to control confounders. RESULTS: The most common age group was 10 to 39 years and 67.9% of the participants were male. The overall prevalence of face mask usage was 45.6% (95% CI, 44.6-46.5). In general, as the age increased, the prevalence of face mask use significantly increased (p for trend < 0.001). Women used face masks significantly higher than men (60.2% vs. 38.7%, p < 0.001). Among the pedestrians who used the mask, 75.6% wore facemask correctly. The most common type of facemask used by the pedestrians were surgical (medical) masks (63.8%). In total, the prevalence of facemask usage was significantly higher during a.m. (49.4%) compared to p.m. (43.9%), (p < 0.001). Besides, in our study, 1.7 and 0.3% of Pedestrians had worn gloves and shielded respectively. Women used shields and gloves significantly higher than men (3.6% vs. 0.7%, p < 0.001). Also, women used shields more than men (0.5% vs. 0.3%, p = 0.036). CONCLUSION: We concluded that the prevalence rate of face mask use in Ahvaz was fairly low especially in men and younger people. Hence, the observed rates probably cannot protect the community against COVID-19 spread. Therefore, it is important to implement educational programs as well as to establish laws and regulations governing the use of face masks in public places.

Estimating density limits for walking pedestrians keeping a safe interpersonal distancing.

With people trying to keep a safe distance from others due to the COVID-19 outbreak, the way in which pedestrians walk has completely changed since the pandemic broke out1,2. In this work, laboratory experiments demonstrate the effect of several variables-such as the pedestrian density, the walking speed and the prescribed safety distance-on the interpersonal distance established when people move within relatively dense crowds. Notably, we observe that the density should not be higher than 0.16 pedestrians per square meter (around 6 m2 per pedestrian) in order to guarantee an interpersonal distance of 1 m. Although the extrapolation of our findings to other more realistic scenarios is not straightforward, they can be used as a first approach to establish density restrictions in urban and architectonic spaces based on scientific evidence.

Trauma patterns during the COVID-19 lockdown in South Africa expose vulnerability of women.

BACKGROUND: Trauma care places a significant burden on the South African (SA) healthcare system, and this has not changed significantly in recent history. We speculated that the COVID-19 lockdown regulations (travel restriction and alcohol ban) would affect trauma patterns. OBJECTIVES: To compare the burden and nature of trauma over the COVID-19 lockdown period with the equivalent period over the past 5 years using routinely collected data from the Pietermaritzburg Metropolitan Trauma Service in KwaZulu-Natal Province, SA. METHODS: All trauma patients admitted to Grey's Hospital in Pietermaritzburg between 23 March 2015 and 31 May 2020 were identified and reviewed. RESULTS: A total of 8 859 trauma patients were admitted over the 6-year period, with a total of 1 676 admitted during the periods 23 March - 31 May. These 1 676 formed the study cohort. Of these patients, 998 had sustained blunt trauma, 665 penetrating trauma, and 13 a combination of blunt and penetrating trauma. A total of 14 categories of blunt trauma were reviewed, of which the three most common were assault, motor vehicle accidents (MVAs) and pedestrian vehicle accidents (PVAs). Between 23 March and 31 May 2020, a total of 23 patients were victims of blunt assault. The median number of assault victims over the equivalent period during the previous 5 years was 48. The 5 preceding years had a median of 56 MVAs and 33 PVAs, compared with 23 and 10 during the lockdown. The median number of gunshot wound (GSW) victims for the preceding years was 41, compared with 30 during the lockdown. During the lockdown, 24 stab wound victims were admitted, compared with a median of 73 for the preceding years. The proportion of females who sustained penetrating trauma and blunt assault increased significantly during the lockdown. The proportion of females sustaining a GSW or blunt trauma secondary to an MVA remained constant. CONCLUSIONS: The study showed that during the period of lockdown in SA there was a significant decrease in MVAs, PVAs and interpersonal violence. Assaults involving a knife seemed to decrease dramatically, but the rate of GSWs remained constant.

Monitoring physical distancing for crowd management: Real-time trajectory and group analysis.

Physical distancing, as a measure to contain the spreading of Covid-19, is defining a "new normal". Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: "in which conditions non-family members infringed social distancing?", "Are there repeated offenders?", and "How are new crowd management measures performing?". Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.

Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread.

There is direct evidence for the spread of infectious diseases such as influenza, SARS, measles, and norovirus in locations where large groups of people gather at high densities e.g. theme parks, airports, etc. The mixing of susceptible and infectious individuals in these high people density man-made environments involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. We address this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. The pedestrian dynamics model is utilized to generate the trajectories of motion and contacts between infected and susceptible individuals. We incorporate this information into a stochastic infection dynamics model with infection probability and contact radius as primary inputs. This generic model is applicable for several directly transmitted diseases by varying the input parameters related to infectivity and transmission mechanisms. Through this multiscale framework, we estimate the aggregate numbers and probabilities of newly infected people for different winding queue configurations. We find that the queue configuration has a significant impact on disease spread for a range of infection radii and transmission probabilities. We quantify the effectiveness of wall separators in suppressing the disease spread compared to rope separators. Further, we find that configurations with short aisles lower the infection spread when rope separators are used.