Social distancing enhanced automated optimal design of physical spaces in the wake of the COVID-19 pandemic

The impact of COVID-19 on visitors' wayfinding within healthcare centers

The novel COVID-19 pandemic has caused substantial calamities in developing countries such as Iran, which initially suffered from inadequate infrastructure essential for the pandemic control. Due to the ongoing development of this malady, healthcare centers are recognized as one of the most significant hotspots within public settings so they are directly pertinent to the physical and mental health of visitors. The main objective for conducting the present study is to investigate the impact of the COVID-19 pandemic on the visitors' wayfinding procedure within Qa'em hospital, located in Rasht, northern Iran. The adopted methodology in the present study is based on a comparison between the collected data regarding the wayfinding behavior of visitors before and after the outbreak of the COVID-19 pandemic using mixed methods, namely Space Syntax, gate counting, people following, and semi-structured interviews. The obtained empirical results displayed that visitors were significantly confused and hesitant throughout their wayfinding process after the outbreak of the pandemic. Indeed, spatial accessibility and legibility were not found to be adequate for facilitating the wayfinding of the visitors. Moreover, the requirements for the reconfiguration of furniture layout in the waiting areas, according to the underlying notions of social distancing, became conspicuous as the pragmatic implications for the post-pandemic healthcare centers.

Physical distancing as an integral component of pandemic response

It is well established that a variety of physical distancing measures are invaluable as part of the overall response to pandemics. COVID-19 is the most recent such pandemic, a respiratory disease transmitted through interaction, necessitating steps to minimize or eliminate the potential for exposure. Of course, this is driven by a desire to keep the economy moving, allow for social activity, continue education, support the livelihoods of individuals, etc. Regional science and supporting analytics have an important role in managing activity through the development and application of methods that enable spatial interaction that mitigates transmission. This paper details methods to plan for physical distancing at micro-scales, enabling the return of social, economic, entertainment, etc. activities. Geographic information systems combined with spatial optimization offers important spatial coronametrics for the mitigation of risk in disease transmission. Applications detailing office space occupancy and travel along with room seating are highlighted.

Safe distancing in the time of COVID-19

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area-a policy that significantly reduces the overall risk of contagion.

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the "COVID-19 safe campus" evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers' evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights.

On the optimal layout of a dining room in the era of COVID-19 using mathematical optimization

We consider the problem of maximizing the number of people that a dining room can accommodate provided that the chairs belonging to different tables are socially distant. We introduce an optimization model that incorporates several characteristics of the problem, namely: the type and size of surface of the dining room, the shapes and sizes of the tables, the positions of the chairs, the sitting sense of the customers, and the possibility of adding space separators to increase the capacity. We propose a simple, yet general, set-packing formulation for the problem. We investigate the efficiency of space separators and the impact of considering the sitting sense of customers in the room capacity. We also perform an algorithmic analysis of the model, and assess its scalability to the problem size, the presence of (or lack thereof) room separators, and the consideration of the sitting sense of customers. We also propose two constructive heuristics capable of coping with large problem instances otherwise intractable for the optimization model.

Assessing personal exposure to COVID-19 transmission in public indoor spaces based on fine-grained trajectory data: A simulation study

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manual interview, which is costly and time consuming given the large population involved. With advanced indoor localisation techniques, it is possible to collect people's footprints accurately by locating their smartphones. This study presents a new framework for the assessment of personal exposure to COVID-19 carriers using their fine-grained trajectory data. An integral model was established to quantify the exposure risk, in which the spatial and temporal decay effects are simultaneously considered when modelling the airborne transmission of COVID-19. Regarding the obstacle effect of the indoor layout on airborne transmission, a weight graph based on the space syntax technique was further introduced to constrain the transmission strength between subspaces that are less inter-visible. The proposed framework was demonstrated by a simulation study, in which external comparison and internal analysis were conducted to justify its validity and robustness in different scenarios. Our method is expected to promote the efficient identification of potential infectees and provide an extensible spatial-temporal model to simulate different control measures and examine their effectiveness in a built environment.

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs

The COVID-19 pandemic has made transportation hubs vulnerable to public health risks. In response, policies using nonpharmaceutical interventions have been implemented, changing the way individuals interact within these facilities. However, the impact of building design and operation on policy efficacy is not fully discovered, making it critical to investigate how these policies are perceived and complied in different building spaces. Therefore, we investigate the spatial drivers of user perceptions and policy compliance in airports. Using text mining, we analyze 103,428 Google Maps reviews of 64 major hub airports in the US to identify representative topics of passenger concerns in airports (i.e., Staff, Shop, Space, and Service). Our results show that passengers express having positive experiences with Staff and Shop, but neutral or negative experiences with Service and Space, which indicates how building design has impacted policy compliance and the vulnerability of health crises. Furthermore, we discuss the actual review comments with respect to 1) spatial design and planning, 2) gate assignment and operation, 3) airport service policy, and 4) building maintenance, which will construct the foundational knowledge to improve the resilience of transportation hubs to future health crises.

A support tool for planning classrooms considering social distancing between students

In this paper, we present the online tool http://salaplanejada.unifesp.br, developed to assist the layout planning of classrooms considering the social distancing in the context of the COVID-19 pandemic. We address both the fixed and non-fixed position seat allocation problems. For the first case, we use two integer optimization models and discuss some curiosities about the solutions found. For the case that the seats can be moved freely, we handle the problem with circle packing techniques using continuous non-linear optimization. For these instances, we propose new algorithms, following other packing problems approaches in the literature. In addition, we propose a fast heuristic that provides a good starting point for the optimization procedure and also an efficient configuration ensuring the students positions in lines, which may be of interest to the user. Computational results are presented to illustrate the numerical behavior of the algorithms and models.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

COVID-19 spread in a classroom equipped with partition - A CFD approach

In this study, the motion and distribution of droplets containing coronaviruses emitted by coughing of an infected person in front of a classroom (e.g., a teacher) were investigated using CFD. A 3D turbulence model was used to simulate the airflow in the classroom, and a Lagrangian particle trajectory analysis method was used to track the droplets. The numerical model was validated and was used to study the effects of ventilation airflow speeds of 3, 5, and 7 m/s on the dispersion of droplets of different sizes. In particular, the effect of installing transparent barriers in front of the seats on reducing the average droplet concentration was examined. The results showed that using the seat partitions for individuals can prevent the infection to a certain extent. An increase in the ventilation air velocity increased the droplets' velocities in the airflow direction, simultaneously reducing the trapping time of the droplets by solid barriers. As expected, in the absence of partitions, the closest seats to the infected person had the highest average droplet concentration (3.80 × 10^-8 kg/m³ for the case of 3 m/s).

Investigating the Links Between Vaccination Against COVID-19 and Public Attitudes Toward Protective Countermeasures: Implications for Public Health

The COVID-19 pandemic caused by the novel coronavirus, SARS-CoV-2, is spreading globally at an unprecedented rate. To protect the world against this devastating catastrophe, vaccines for SARS-CoV-2 have been produced following consistent clinical trials. However, the durability of a protective immune response due to vaccination has not been confirmed. Moreover, COVID-19 vaccination against SARS-CoV-2 is not 100% guaranteed, as new variants arise due to mutations. Consequently, health officials are pleading with the public to take extra precautions against the virus and continue wearing masks, wash hands, and observe physical distancing even after vaccination. The current research collected data from 4,540 participants (1,825 vaccinated and 2,715 not vaccinated) in China to analyze this phenomenon empirically. The propensity score matching (PSM) model is employed to analyze the impact of vaccination against COVID-19 on participants' attitudes toward protective countermeasures. The findings showed that gender, age, education level, occupation risk, individual health risk perception, public health risk perception, social responsibility, peer effect, and government supervision are the main drivers for participants to be vaccinated with COVID-19's vaccines. The results further show that vaccination lessened participants' frequency of hand washing by 1.75 times and their compliance frequency intensity of observing physical distancing by 1.24 times. However, the rate of mask-wearing did not reduce significantly, implying that China's main countermeasure of effective mask-wearing effectively controls COVID-19. Moreover, the findings indicate that a reduction in the frequency of hand washing and observing physical distance could cause a resurgence of COVID-19. In conclusion, factors leading to the eradication of SARS-CoV-2 from the world are complex to be achieved, so the exploration of COVID-19 vaccination and people's attitude toward protective countermeasures may provide insights for policymakers to encourage vaccinated people to follow protective health measures and help in completely defeating the COVID-19 from the globe.