Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data

Estimation of contact patterns is often based on questionnaires and time-use data. The results obtained using these methods have been used extensively over the years and recently to predict the spread of the COVID-19 pandemic. They have also been used to test the effectiveness of non-pharmaceutical measures such as social distance. The latter is integrated into epidemiological models by multiplying contact matrices by control functions. We present a novel method that allows the integration of social distancing and other scenarios such as panic. Our method is based on a modified social force model. The model is calibrated using data relating to the movements of individuals and their interactions such as desired walking velocities and interpersonal distances as well as demographic data. We used the framework to assess contact patterns in different social contexts in Morocco. The estimated matrices are extremely assortative and exhibit patterns similar to those observed in other studies including the POLYMOD project. Our findings suggest social distancing would reduce the numbers of contacts by 95%. Further, we estimated the effect of panic on contact patterns, which indicated an increase in the number of contacts of 11%. This approach could be an alternative to questionnaire-based methods in the study of non-pharmaceutical measures and other specific scenarios such as rush hours. It also provides a substitute for estimating children's contact patterns which are typically assessed through parental proxy reporting in surveys.

The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.

Modeling the impact of distancing measures on infectious disease spread: a case study of COVID-19 in the Moroccan population

This paper explores the impact of various distancing measures on the spread of infectious diseases, focusing on the spread of COVID-19 in the Moroccan population as a case study. Contact matrices, generated through a social force model, capture population interactions within distinct activity locations and age groups. These matrices, tailored for each distancing scenario, have been incorporated into an SEIR model. The study models the region as a network of interconnected activity locations, enabling flexible analysis of the effects of different distancing measures within social contexts and between age groups. Additionally, the method assesses the influence of measures targeting potential superspreaders (i.e., agents with a very high contact rate) and explores the impact of inter-activity location flows, providing insights beyond scalar contact rates or survey-based contact matrices. The results suggest that implementing intra-activity location distancing measures significantly reduces in the number of infected individuals relative to the act of imposing restrictions on individuals with a high contact rate in each activity location. The combination of both measures proves more advantageous. On a regional scale, characterized as a network of interconnected activity locations, restrictions on the movement of individuals with high contact rates was found to result in a $ 2 \% $ reduction, while intra-activity location-based distancing measures was found to achieve a $ 44 \% $ reduction. The combination of these two measures yielded a $ 48\% $ reduction.

A Managerial Approach towards Modeling the Different Strains of the COVID-19 Virus Based on the Spatial GeoCity Model

This study proposes a modification of the GeoCity model previously developed by the authors, detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents. This made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of three types of strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. Calculations based on the proposed model show that SARS-CoV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the recipients, not the initiators of the epidemic. The simulations showed that fluctuations in the dynamics of various indicators of the spread of SARS-CoV 2 were associated with the difference in the daily schedule on weekdays and weekends. The results of the calculations showed that the daily schedules of people strongly influence the spread of SARS-CoV 2. Under assumptions of the model, the results show that for the more contagious "rapid" strains of SARS-CoV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS-CoV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious, or "fast" strain of the SARS-CoV 2 virus (omicron), spreads faster in public transport. For less contagious, or "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model

With the spatial structure of urban agglomerations, well-developed transportation networks and close economic ties can increase the risk of intercity transmission of infectious diseases. To reveal the epidemic transmission mechanism in urban agglomerations and to explore the effectiveness of traffic control measures, this study proposes an Urban-Agglomeration-based Epidemic and Mobility Model (UAEMM) based on the reality of urban transportation networks and population mobility factors. Since the model considers the urban population inflow, along with the active intracity population, it can be used to estimate the composition of urban cases. The model was applied to the Chang-Zhu-Tan urban agglomeration, and the results show that the model can better simulate the transmission process of the urban agglomeration for a certain scale of epidemic. The number of cases within the urban agglomeration is higher than the number of cases imported into the urban agglomeration from external cities. The composition of cases in the core cities of the urban agglomeration changes with the adjustment of prevention and control measures. In contrast, the number of cases imported into the secondary cities is consistently greater than the number of cases transmitted within the cities. A traffic control measures discount factor is introduced to simulate the development of the epidemic in the urban agglomeration under the traffic control measures of the first-level response to major public health emergency, traffic blockades in infected areas, and public transportation shutdowns. If none of those traffic control measures had been taken after the outbreak of COVID-19, the number of cases in the urban agglomeration would theoretically have increased to 3879, which is 11.61 times the actual number of cases that occurred. If only one traffic control measure had been used alone, each of the three measures would have reduced the number of cases in the urban agglomeration to 30.19 %-57.44 % of the theoretical values of infection cases, with the best blocking effect coming from the first-level response to major public health emergency. Traffic control measures have a significant effect in interrupting the spread of COVID-19 in urban agglomerations. The methodology and main findings presented in this paper are of general interest and can also be used in studies in other countries for similar purposes to help understand the spread of COVID-19 in urban agglomerations.

Virus transmission risk of college students in railway station during Post-COVID-19 era: Combining the social force model and the virus transmission model

In the post-epidemic era, people's lives are gradually returning to normal, and travel is gradually resuming. The safe evacuation of cross-regional travelers in railway station has also attracted more and more attention, especially the evacuation behavior of college students in railway station. In this paper, considering the pedestrian dynamics mechanism in the emergency evacuation process during the COVID-19 normalized epidemic prevention and control, an Agent-based social force model was established to simulate the activities of college students in railway station. Combined with the virus infection transmission model, Monte Carlo simulation was used to calculate the total exposure time and the number of high-risk exposed people in the railway station evacuation process. In addition, sensitivity analysis was conducted on the total exposure time and the number of high-risk exposed people under 180 combinations of the number of initial infections, social distance, and the proportion of people wearing masks incorrectly. The results show that with the increase of social distances, the total exposure time and the number of high-risk exposures do not always decrease, but increase in some cases. The presence or absence of obstacles in the evacuation scene has no significant difference in the effects on total exposure time and the number of high-risk exposures. During the evacuation behavior of college students in railway station, choosing the appropriate number of lines can effectively reduce the total exposure time and the number of high-risk exposures. Finally, some policy suggestions are proposed to reduce the risk of virus transmission in the railway station evacuation process, such as choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles.

Crowd dynamics research in the era of Covid-19 pandemic: Challenges and opportunities

With the issues of crowd control and physical distancing becoming central to disease prevention measures, one would expect that crowd research should become a focus of attention during the Covid-19 pandemic era. However, I will show, based on a variety of metrics, that not only has this not been the case, but also, the first two years of the pandemic have posed an undisputable setback to the development and growth of crowd science. Without intervention, this could potentially aggravate further and cause a long-lasting recession in this field. This article, in addition to documenting and highlighting this issue, aims to outline potential avenues through which crowd research can reshape itself in the era of Covid-19 pandemic, maintain its pre-pandemic momentum and even further expand the diversity of its topics. Despite significant changes that the pandemic has brought to human life, issues related to congregation and mobility of pedestrians, building fires, crowd incidents, rallying crowds and the like have not disappeared from societies and remain relevant. Moreover, the diversity of pandemic-related problems itself creates a rich ground for making novel scientific discoveries. This could provide grounds for establishing fresh dimensions in crowd dynamics research. These potential new dimensions extend to all areas of this field including numerical and experimental investigations, crowd psychology and applications of computer vision and artificial intelligence methods in crowd management. The Covid-19 pandemic may have posed challenges to crowd researchers but has also created ample potential opportunities. This is further evidenced by reviewing efforts taken thus far in pandemic-related crowd research.

Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level

Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

Transmission in home environment associated with the second wave of COVID-19 pandemic in India

India has suffered from the second wave of COVID-19 pandemic since March 2021. This wave of the outbreak has been more serious than the first wave pandemic in 2020, which suggests that some new transmission characteristics may exist. COVID-19 is transmitted through droplets, aerosols, and contact with infected surfaces. Air pollutants are also considered to be associated with COVID-19 transmission. However, the roles of indoor transmission in the COVID-19 pandemic and the effects of these factors in indoor environments are still poorly understood. Our study focused on reveal the role of indoor transmission in the second wave of COVID-19 pandemic in India. Our results indicated that human mobility in the home environment had the highest relative influence on COVID-19 daily growth rate in the country. The COVID-19 daily growth rate was significantly positively correlated with the residential percent rate in most state-level areas in India. A significant positive nonlinear relationship was found when the residential percent ratio ranged from 100 to 120%. Further, epidemic dynamics modelling indicated that a higher proportion of indoor transmission in the home environment was able to intensify the severity of the second wave of COVID-19 pandemic in India. Our findings suggested that more attention should be paid to the indoor transmission in home environment. The public health strategies to reduce indoor transmission such as ventilation and centralized isolation will be beneficial to the prevention and control of COVID-19.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model's validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

Pedestrian movement modelling for a commercial street considering COVID-19 social distancing strategies

This research attempts to understand the impacts of social distancing on dense urban pedestrian environments through pedestrian movement simulations. It develops a pedestrian microsimulation modelling framework that evaluates three scenarios for a commercial street in the Halifax Regional Municipality (HRM). The Business-as-Usual scenario mimics pre-COVID conditions with no social distancing protocols. Pandemic Scenario# 1 represents social distancing without any changes in the pedestrian infrastructure. The HRM has adopted a mobility response plan for COVID-19, this generates Pandemic Scenario# 2 depicting the widened sidewalks within the pedestrian microsimulation model. The results reveal that the social distancing strategy in the pandemic scenarios significantly improved pedestrian flow in terms of the reduction in contact violations. These violations are described as instances in which a pedestrian violates the 2 m social distancing rule. The simulation of the first pandemic scenario (no sidewalk enhancement) showed a significant reduction of 43% in the number of contact violations during the one-hour pedestrian simulation of the street. The second pandemic scenario showed a 68% decrease in violations. The conclusions derived from this research support the actions of the municipality as the simulation results indicate that an increase in sidewalk width can influence contact rates and time travelled. When comparing the two pandemic scenarios, the scenario that incorporated wider sidewalks showed a decrease in total travel time and contact rates.

Model-based assessment of the risks of viral transmission in non-confined crowds

This work assesses the risks of Covid-19 spread in diverse daily-life situations involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations, by coupling ad hoc spatial models for disease transmission via respiratory droplets to detailed field-data about pedestrian trajectories and head orientations. This allows us to rank the investigated situations by the infection risks that they present; importantly, the obtained hierarchy of risks is very largely conserved across transmission models: Street cafés present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets are comparatively quite low. While our models only approximate the actual transmission risks, their converging predictions lend credence to these findings. In situations with a moving crowd, density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.

Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic

During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.

Social distancing in pedestrian dynamics and its effect on disease spreading

Nonpharmaceutical measures such as social distancing can play an important role in controlling the spread of an epidemic. In this paper, we use a mathematical model combining human mobility and disease spreading. For the mobility dynamics, we design an agent-based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites. For the spreading dynamics, we consider the compartmental susceptible-exposed-infective (SEI) dynamics plus an indirect transmission with the footprints of the infectious pedestrians being the contagion factor. We show that the increase in the intensity of social distancing has a significant effect on the exposure risk. By classifying the population into social distancing abiders and nonabiders, we conclude that the practice of social distancing, even by a minority of potentially infectious agents, results in a drastic change in the population exposure risk, but it reduces the effectiveness of the protocols when practiced by the rest of the population. Furthermore, we observe that for contagions for which the indirect transmission is more significant, the effectiveness of social distancing would be reduced. This study can help to provide a quantitative guideline for policy-making on exposure risk reduction.

Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.