Modeling epidemic spreading through public transit using time-varying encounter network

Co-evolution model of traffic travel and disease transmission under limited resources

The co-evolution mechanisms between traffic mobility and disease transmission under resource constraints remain poorly understood. This study proposes a two-layer transportation network model integrating the Susceptible-Infectious-Susceptible (SIS) epidemic framework to address this gap. The model incorporates critical factors such as total medical resources, inter-network infection delays, travel willingness, and network topology. Through simulations, we demonstrate that increasing medical resources significantly reduces infection scale during outbreaks, while prolonging inter-network delays slows transmission rates but extends epidemic persistence. Complex network topologies amplify the impact of travel behavior on disease spread, and multi-factor interventions (e.g., combined resource allocation and delay extension) outperform single-factor controls in suppressing transmission. Furthermore, reducing network connectivity (lower average degree) proves effective in mitigating outbreaks, especially under low travel willingness. These findings highlight the necessity of coordinated policies that leverage resource optimization, travel regulation, and network simplification to manage epidemics. This work provides actionable insights for policymakers to design efficient epidemic control strategies in transportation-dependent societies.

Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data

The SARS-CoV-2 virus is primarily transmitted through in-person interactions, and so its growth in urban space is a complex function of human mobility behaviors that cannot be adequately explained by standard epidemiological models. Recent studies leveraged fine-grained urban mobility data to accurately model the viral spread, but such data pose privacy concerns and are often difficult to collect, especially in low- and middle-income countries (LMICs). Here, we show that the metapopulation epidemiological model incorporated with a simple gravity mobility model can be sufficient to capture most of the complex epidemic dynamics in urban space, largely reducing the need for empirical mobility data. Extensive experiments on 30 cities in the United States, India and Brazil show that our model consistently reproduces complex, distinctive COVID-19 growth curves with high accuracy. It also provides a theoretical explanation of the emergence of urban "superspreading", where a few high-risk neighborhoods account for most subsequent infections. Furthermore, with the aid of the proposed framework, we can inform mobility-aware travel restrictions to achieve a better balance between social cost and disease prevention, which facilitates sustainable epidemic control and supports the gradual transition to a post-pandemic world.

Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios: A case study of COVID-19

Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.

Mobile COVID-19 testing program in Phoenix: A retrospective observational cohort study of results, trends, and positivity rates

Over 3 years since the onset of the coronavirus (COVID-19), the COVID-19 pandemic remains a global health challenge. At the same time, review of the response to the current pandemic is required for planning for future pandemics and global health crises. Approximately 2.5 million cases of COVID-19 have been reported in Arizona, a state with a 7.2 million population. Analyzing trends in COVID-19 case and positivity rates is crucial in planning to ensure public health safety for both this and future pandemics. This current observational study analyzes the trends in COVID-19 testing and positivity rates in the Phoenix metropolitan area, from data collected from a mobile testing program between December 2020 and April 2022. A total of 72,827 COVID-19 tests were performed, with a total of 8666 positive cases, yielding an overall positivity rate of 11.9%. Case counts and positivity rates increased during the fall and winter months, peaking in January (January 2021: 13.96% and January 2022: 24.84%). These cyclical trends cyclical can help with planning and mitigation. Continued public health awareness, including vaccinations and testing, is required in controlling COVID-19 transmission.

Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review

OBJECTIVES

To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission.

METHODS

a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases.

RESULTS

Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common.

CONCLUSION

We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.

An evaluation framework for operational interventions on urban mass public transport during a pandemic

Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3-195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic.

A topology-based evaluation of resilience on urban road networks against epidemic spread: Implications for COVID-19 responses

Road closure is an effective measure to reduce mobility and prevent the spread of an epidemic in severe public health crises. For instance, during the peak waves of the global COVID-19 pandemic, many countries implemented road closure policies, such as the traffic-calming strategy in the UK. However, it is still not clear how such road closures, if used as a response to different modes of epidemic spreading, affect the resilient performance of large-scale road networks in terms of their efficiency and overall accessibility. In this paper, we propose a simulation-based approach to theoretically investigate two types of spreading mechanisms and evaluate the effectiveness of both static and dynamic response scenarios, including the sporadic epidemic spreading based on network topologies and trajectory-based spreading caused by superspreaders in megacities. The results showed that (1) the road network demonstrates comparatively worse resilient behavior under the trajectory-based spreading mode; (2) the road density and centrality order, as well as the network's regional geographical characteristics, can substantially alter the level of impacts and introduce heterogeneity into the recovery processes; and (3) the resilience lost under static recovery and dynamic recovery scenarios is 8.6 and 6.9%, respectively, which demonstrates the necessity of a dynamic response and the importance of making a systematic and strategic recovery plan. Policy and managerial implications are also discussed. This paper provides new insights for better managing the resilience of urban road networks against public health crises in the post-COVID era.

Effects of official information and rumor on resource-epidemic coevolution dynamics

Epidemic-related information and resources have proven to have a significant impact on the spread of the epidemic during the Corona Virus Disease 2019 (COVID-19) pandemic. The various orientation role of information has different effects on the epidemic spreading process, which will affect the individual’ awareness of resources allocation and epidemic spreading scale. Based on this, a three-layer network is established to describe the dynamic coevolution process among information dissemination, resource allocation, and epidemic spreading. In order to analyze dynamic coevolution process, the microscopic Markov chain (MMC) theory is used. Then, the threshold of epidemic spreading is deduced. Our results indicated that the official information orientation intensity inhibits the epidemics spreading, while rumor orientation intensity promotes epidemic spreading. At the same time, the efficiency of resource utilization restrains the expansion of the infection scale. The two kinds of information are combined with resources respectively. Official information will enhance the inhibitory effect of resources epidemics spreading, while rumor will do the opposite.

Transmission and control of SARS-CoV-2 on ground public transport: A rapid review of the literature up to May 2021

Background

During a pandemic, public transport is strategically important for keeping the country going and getting people where they need to be. The essential nature of public transport puts into focus the risk of transmission of SARS-CoV-2 in this sector; rapid and diverse work has been done to attempt to understand how transmission happens in this context and what factors influence risk.

Objectives

This review aimed to provide a narrative overview of the literature assessing transmission, or potential for transmission, of SARS-CoV-2 on ground-based public transport, as well as studies assessing the effectiveness of control measures on public transport during the early part of the pandemic (up to May 2021).

Methods

An electronic search was conducted using Web of Science, Ovid, the Cochrane Library, ProQuest, Pubmed, and the WHO global COVID database. Searches were run between December 2020 and May 2021.

Results

The search strategy identified 734 papers, of which 28 papers met the inclusion criteria for the review; 10 papers assessed transmission of SARS-CoV-2, 11 assessed control measures, and seven assessed levels of contamination. Eleven papers were based on modelling approaches; 17 studies were original studies reporting empirical COVID-19 data.

Conclusions

The literature is heterogeneous, and there are challenges for measurement of transmission in this setting. There is evidence for transmission in certain cases, and mixed evidence for the presence of viral RNA in transport settings; there is also evidence for some reduction of risk through mitigation. However, the routes of transmission and key factors contributing to transmission of SARS-CoV-2 on public transport were not clear during the early stage of the pandemic. Gaps in understanding are discussed and six key questions for future research have been posed. Further exploration of transmission factors and effectiveness of mitigation strategies is required in order to support decision making.

Revealing spatiotemporal interaction patterns behind complex cities

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals. The rank-size distributions of dynamic population of locations in all unit time windows are stable, although people are almost constantly moving in cities and hot-spots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatiotemporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities, while in the "inactive" state, people are scattered in smaller communities. Above discoveries are universal over three cities across continents. In addition, a city stays in an active state for a longer time when its population grows larger. Spatiotemporal interaction segregation can be well approximated by residential patterns only in smaller cities. In addition, we propose a temporal-population-weighted-opportunity model by integrating a time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain the observed patterns of spatiotemporal interactions in cities.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Post pandemic fatigue: what are effective strategies?

Recurrent updates in non-pharmaceutical interventions (NPIs) aim to control successive waves of the coronavirus disease 2019 (COVID-19) but are often met with low adherence by the public. This study evaluated the effectiveness of gathering restrictions and quarantine policies based on a modified Susceptible-Exposed-Infectious-Hospitalized-Recovered (SEIHR) model by incorporating cross-boundary travellers with or without quarantine to study the transmission dynamics of COVID-19 with data spanning a nine-month period during 2020 in Hong Kong. The asymptotic stability of equilibria reveals that the model exhibits the phenomenon of backward bifurcation, which in this study is a co-existence between a stable disease-free equilibrium (DFE) and an endemic equilibrium (EE). Even if the basic reproduction number ([Formula: see text]) is less than unity, this disease cannot be eliminated. The effect of each parameter on the overall dynamics was assessed using Partial Rank Correlation Coefficients (PRCCs). Transmission rates (i.e., [Formula: see text] and [Formula: see text]), effective contact ratio [Formula: see text] between symptomatic individuals and quarantined people, and transfer rate [Formula: see text] related to infection during quarantine were identified to be the most sensitive parameters. The effective contact ratios between the infectors and susceptible individuals in late July were found to be over twice as high as that in March of 2020, reflecting pandemic fatigue and the potential existence of infection during quarantine.

SARS-CoV-2 Dissemination Using a Network of the US Counties

During 2020 and 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been increasing among the world’s population at an alarming rate. Reducing the spread of SARS-CoV-2 and other diseases that are spread in similar manners is paramount for public health officials as they seek to effectively manage resources and potential population control measures such as social distancing and quarantines. By analyzing the US county network structure, one can model and interdict potential higher infection areas. County officials can provide targeted information, preparedness training, and increase testing the researchers conclude that traditional the researchers conclude that traditional in these areas. While these approaches may provide adequate countermeasures for localized areas, they are inadequate for the holistic USA. We solve this problem by collecting coronavirus disease 2019 (COVID-19) infections and deaths from the Center for Disease Control and Prevention, and adjacency between all counties obtained from the United States Census Bureau. Generalized network autoregressive (GNAR) time series models have been proposed as an efficient learning algorithm for networked datasets. This work fuses network science and operations research techniques to univariately model COVID-19 cases, deaths, and current survivors across the US county network structure.

Managing public transit during a pandemic: The trade-off between safety and mobility

During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers' proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.

Assessing the impact of mobility on the incidence of COVID-19 in Dublin City

COVID-19 has had a major impact on the transport systems around the world. Several transport-related policies were implemented in short period of time to contain the spread of the pandemic. These policies had a major influence on travel behavior and people's perception towards the safety of different modes of transport, especially public transport, thus affecting several sustainable mobility initiatives. To build a resilient and sustainable transport system and to rebuild trust in public transport, it is important to understand the role of mobility in the spread of COVID-19 pandemic. The present study investigates the relationship between mobility and reported COVID-19 infections using data from Dublin city. Different modes of transport including traffic volume, bus passengers, pedestrians and cyclists were considered in the study during a forty week period. Multiple scenarios involving two-week lag and three-week lag of mobility data and COVID-19 infections were considered in building statistical models. Results showed that, 36.2% of the reported COVID-19 infections after a two-week lag and 33% of the infections after a three-week lag. Our research examines the links between movements and COVID-19 numbers, but clearly this was not the only reason for increased case numbers as many other events impacted on increased numbers. The study further discusses the policy implications and strategies for ensuring a resilient and sustainable transport system.

Transit use reduction following COVID-19: The effect of threat appraisal, proactive coping and institutional trust

Transit systems suffered from a significant demand decrease during COVID-19. Understanding the psychological motivators underlying reduced transit use can help transit authorities and operators to take proactive action towards returning to the "new normal" and increasing their preparedness towards future pandemics. This study is based on the protection motivation theory to understand the effect of threat appraisal, and coping appraisal and denial mechanisms on transit use reduction for commuting. The behavioral framework is validated by a survey of 856 transit users in Israel during August 2020, three months after the end of the lockdown and before the vaccine administration. The results show that: i) Skepticism, risk ubiquity, and personal immunity beliefs lead to maladaptive threat appraisal; ii) wearing masks and social distancing are antecedents of fear of infection while using transit and reduced transit use; iii) higher perceived threat deters transit use, while trust in transit operators motivates transit use; and iv) in a franchised transit system, trust in transit operators depends on the perceived level-of-service and trust in the ability of government authorities to regulate, monitor and enforce transit operators' preventive and protective actions.

Managing public transit during a pandemic: The trade-off between safety and mobility

During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers' proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.

A knowledge graph-based method for epidemic contact tracing in public transportation

Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.

Impact of mobility on COVID-19 spread - A time series analysis

In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in the number of infected people. In addition, the results confirmed that super-spreading events had significant increases in the number of positive cases. We have also assessed the impact of major events and holidays throughout the study period and analyzed the impacts of mobility patterns in those situations. Our analysis shows that holidays without inter-city travel bans have been associated with a 27% increase in the number of weekly positive cases. As such, while holidays decrease transit usage, it can overall negatively affect spread control if proper control measures are not put in place. The result and discussions in this paper can help authorities understand the effects of different strategies and protocols with a pandemic control and choose the most beneficial ones.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

The influence of a transport process on the epidemic threshold

By generating transient encounters between individuals beyond their immediate social environment, transport can have a profound impact on the spreading of an epidemic. In this work, we consider epidemic dynamics in the presence of the transport process that gives rise to a multiplex network model. In addition to a static layer, the (multiplex) epidemic network consists of a second dynamic layer in which any two individuals are connected for the time they occupy the same site during a random walk they perform on a separate transport network. We develop a mean-field description of the stochastic network model and study the influence the transport process has on the epidemic threshold. We show that any transport process generally lowers the epidemic threshold because of the additional connections it generates. In contrast, considering also random walks of fractional order that in some sense are a more realistic model of human mobility, we find that these non-local transport dynamics raise the epidemic threshold in comparison to a classical local random walk. We also test our model on a realistic transport network (the Munich U-Bahn network), and carefully compare mean-field solutions with stochastic trajectories in a range of scenarios.

A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Epidemiological models have a vital and consolidated role in aiding decision-making during crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. However, the influence of social interactions in the spreading of communicable diseases is left aside from the main models in the literature. The main contribution of this work is the introduction of a probabilistic simulation model based on a multi-agent approach that is capable of predicting the spreading of diseases. Our proposal has a simple model for the main source of infections in pandemics of respiratory viruses: social interactions. This simplicity is key for incorporating complex networks topology into the model, which is a more accurate representation for real-world interactions. This flexibility in network structure allows the evaluation of specific phenomena, such as the presence of super-spreaders. We provide the modeling for the dynamical network topology in two different simulation scenarios. Another contribution is the generic microscopic model for infection evolution that enables the evaluation of impact from more specific behaviors and interventions on the overall spreading of the disease. It also enables a more intuitive process for going from data to model parameters. This ease of changing the infection evolution model is key for performing more complete analyses than would be possible in other models from the literature. Further, we give specific parameters for a controlled scenario with quick testing and tracing. We present computational results that illustrate the model utilization for predicting the spreading of COVID-19 in a city. Also, we show the results of applying the model for assessing the risk of resuming on-site activities at a collective use facility.

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

Safe traveling in public transport amid COVID-19

Several intensive policies, such as mandatorily wearing masks and practicing social distancing, have been implemented in South Korea to prevent the spread of the novel coronavirus disease (COVID-19). We analyzed and measured the impact of the aforementioned policies by calculating the degree of infection exposure in public transport. Specifically, we simulated how passengers encounter and infect each other during their journeys in public transport by tracking movements of passengers. The probabilities of exposure to infections in public transport via the aforementioned preventive measures were compared by using the Susceptible, Exposed, Infected, and Recovered model, a respiratory infectious disease diffusion model. We determined that the mandatory wearing of masks exhibits effects similar to maintaining 2-m social distancing in preventing COVID-19. Mandatory wearing of masks and practicing social distancing with masks during peak hours reduced infection rates by 93.5 and 98.1%, respectively.

Analysis and monitoring of post-COVID mobility demand in Rome resulting from the adoption of sustainable mobility measures

The paper describes research activities of monitoring, modeling, and planning of people mobility in Rome during the Covid-19 epidemic period from March to June 2020. The results of data collection for different transport modes (walking, bicycle, car, and transit) are presented and analyzed. A specific focus is provided for the subway mass transit, where 1 m interpersonal distancing is required to prevent the risks for Covid-19 contagion together with the use of masks and gloves. A transport system model has been calibrated on the data collected during the lockdown period -when people's behavior significantly changed because of smart-working adoption and contagion fear- and was applied to predict future mobility scenarios under different assumptions on economic activities restarting. Based on the estimations of passenger loading, a timing policy that differentiates the opening hours of the shops depending on their commercial category was implemented, and an additional bus transit service was introduced to avoid incompatible loads of the subway lines with the required interpersonal distancing.

Spatiotemporal variation of the worldwide air transportation network induced by COVID-19 pandemic in 2020

This paper studies the spatiotemporal variation of the worldwide air transportation network (WATN) induced by the COVID-19 pandemic in 2020. The variations are captured from four perspectives: passenger throughput, network connectivity, airport centrality, and international connections. Further, this work also considers both global and local connectivity-based metrics for the network analysis. Supported by real-world data, we show that the performance of the WATN has experienced a dynamic pattern of decline and recovery in 2020. Interestingly, the network metrics undergo tremendous variations in a very short period after the World Health Organization declared COVID-19 as a pandemic, with the number of flights and connections dropping by more than 40% within only the first four weeks. Intuitively, the passenger throughput's changing rate is highly correlated to confirmed cases' growth rate during the early period of the COVID-19 outbreak. However, the air transport response to the pandemic condition is very diverse among different countries. The major airports in the WATN fluctuate gradually in different pandemic stages, which is further influenced by the domestic pandemic situation that restricts airport operations. Also, the restoration speed of local connectivity is faster than that of global connectivity because the recovery of international aviation is geographically dependent on different policies of travel restriction, conditional openings, and the number of COVID-19 cases. The analysis deepens our understanding to formulate bilateral policies for pandemic-induced ATN design and management.

COVID-19, the Built Environment, and Health

BACKGROUND

Since the dawn of cities, the built environment has both affected infectious disease transmission and evolved in response to infectious diseases. COVID-19 illustrates both dynamics. The pandemic presented an opportunity to implement health promotion and disease prevention strategies in numerous elements of the built environment.

OBJECTIVES

This commentary aims to identify features of the built environment that affect the risk of COVID-19 as well as to identify elements of the pandemic response with implications for the built environment (and, therefore, for long-term public health).

DISCUSSION

Built environment risk factors for COVID-19 transmission include crowding, poverty, and racism (as they manifest in housing and neighborhood features), poor indoor air circulation, and ambient air pollution. Potential long-term implications of COVID-19 for the built environment include changes in building design, increased teleworking, reconfigured streets, changing modes of travel, provision of parks and greenspace, and population shifts out of urban centers. Although it is too early to predict with confidence which of these responses may persist, identifying and monitoring them can help health professionals, architects, urban planners, and decision makers, as well as members of the public, optimize healthy built environments during and after recovery from the pandemic. https://doi.org/10.1289/EHP8888.

Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis

This article maps the scientific literature in human mobility behavior in the context of the current pandemic. Through bibliometrics, we analyze the content of published scientific studies indexed on the Web of Science and Scopus during 2020. This enables us the detection of current hotspots and future directions of research. After a co-occurrence of keywords and evidence map analysis, four themes are identified, namely, Land Transport - Operations, Land Transport - Traffic Demand, Air Transport and Environment. We show how air transportation- and environmental-related studies tend to be more mature research whereas the understanding of changes in travel behavior (e.g., telecommuting, preventive measures or health protection behavior) tends to be immature. By using a topic modeling approach, we identify multiple sub-themes within each theme. Our framework adopts a smart literature review approach that can be constantly updated, enabling an analysis of many articles, with little investment of the researcher's time, but also provides high degree of transparency and replicability. We also put forth a research agenda that can help inform and shape transport policy and practice responses to COVID-19.

Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

A novel methodology for epidemic risk assessment of COVID-19 outbreak

We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.