A human behavior integrated hierarchical model of airborne disease transmission in a large city

Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017

BACKGROUND

The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention.

OBJECTIVE

The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China.

METHODS

We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission.

RESULTS

We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship.

CONCLUSIONS

Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.

Influence of sound and light combined conditions in urban environments on residents' tolerance limits in pre sleep state

To determine the sound and light combined conditions pollution in urban residential environments at night, this paper comprehensively evaluates cross-visual and auditory sensory channels in the laboratory. Experimental variables include extremum and gradient, and the working state of the participants was determined and verified. A subjective evaluation experiment on 18 combined conditions was carried out by synthesizing real-world data. Results from the sound and light combined conditions experiment show that there are significant differences in the tolerance limits of participants to different content sound variables (p = 0.000 < 0.05, p = 0.033 < 0.05, p = 0.002 < 0.05). Among them, the traffic noise (p = 0.000 < 0.05) has the greatest impact on the tolerance limits of people, followed by birdsong (p = 0.033 < 0.05) and human voice (p = 0.002 < 0.05). There is no difference in the tolerance limits of light pollution (p = 0.288 > 0.05, p = 0.122 > 0.05, p = 0.146 > 0.05) at different color temperatures. The tolerance limits of participants will not be reduced due to the superposition of two interference variables: sound pollution and light pollution. Adding light pollution to sound pollution can increase the tolerance limits of participants, while adding sound pollution to light pollution has no significant effect on the tolerance limits. The study also found that adding light with different color temperatures to the human voice can increase participants' tolerance limit to human voice (1% -2%), indicating that visual elements can change individuals' perception of sound. In addition, the physiological and psychological differences between participants may affect the performance differences of individual participants in sound and light combined conditions.

Risk assessment of COVID-19 infection for subway commuters integrating dynamic changes in passenger numbers

The COVID-19 global pandemic has had a significant impact on mass travel. We examined the risk of transmission of COVID-19 infection between subway commuters using the Susceptible Exposed Infected Recovered (SEIR) model. The model considered factors that may influence virus transmission, namely subway disinfection, ventilation capacity, average commuter spacing, single subway journey time, COVID-19 transmission capacity, and dynamic changes in passenger numbers. Based on these parameters, above a certain threshold (25 min), the risk of infection for susceptible people increased significantly as journey time increased. Average distance between commuters and levels of ventilation and disinfection were also important influencing factors. Meanwhile, the model also indicated that the risk of infection varied at different times of the day. Therefore, this paper recommends strengthening ventilation and disinfection in the carriages and limiting the time of single journeys, with an average distance of at least 1 m between passengers. In this light, subway commuters need to take proactive precautions to reduce their risk of COVID-19 infection. Also, the results show the importance of managing subway stations efficiently during epidemic and post-epidemic eras.

How distant? An experimental analysis of students' COVID-19 exposure and physical distancing in university buildings

Closed university buildings proved to be one of the main hot spots for virus transmission during pandemics. As shown during the COVID-19 pandemic, physical distancing is one of the most effective measures to limit such transmission. As universities prepare to manage in-class activities, students' adherence to physical distancing requirements is a priority topic. Unfortunately, while physical distancing in classrooms can be easily managed, the movement of students inside common spaces can pose high risk of close proximity. This paper provides an experimental analysis of unidirectional student movement inside a case-study university building to investigate how physical distancing requirements impact student movement and grouping behaviour. Results show general adherence with the minimum required physical distancing guidance, but spaces such as corridors pose higher risk of exposure than doorways. Doorway width, in combination with group behaviour, affect the students' capacity to keep the recommended physical distance. Furthermore, questionnaire results show that students report higher perceived vulnerability while moving along corridors. Evidence-based results can support decision-makers in understanding individuals' exposure to COVID-19 in universities and researchers in developing behavioural models in preparation of future outbreaks and pandemics.

Microsimulation based quantitative analysis of COVID-19 management strategies

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong

By the end of February 2021, COVID-19 had spread to over 230 countries, with more than 100 million confirmed cases and 2.5 million deaths. To control infection spread with the least disruption to economic and societal activities, it is crucial to implement the various interventions effectively. In this study, we developed an agent-based SEIR model, using real demographic and geographic data from Hong Kong, to analyse the efficiency of various intervention strategies in preventing infection by the SARS-CoV-2 virus. Close contact route including short-range airborne is considered as the main transmission routes for COVID-19 spread. Contact tracing is not that useful if all other interventions have been fully deployed. The number of infected individuals could be halved if people reduced their close contact rate by 25%. For reducing transmission, students should be prioritized for vaccination rather than retired older people and preschool aged children. Home isolation, and taking the nucleic acid test (NAT) as soon as possible after symptom onset, are much more effective interventions than wearing masks in public places. Temperature screening in public places only disrupted the infection spread by a small amount when other interventions have been fully implemented. Our results may be useful for other highly populated cities, when choosing their intervention strategies to prevent outbreaks of COVID-19 and similar diseases.

Sustainable and resilient strategies for touristic cities against COVID-19: An agent-based approach

Touristic cities will suffer from COVID-19 emergency because of its economic impact on their communities. The first emergency phases involved a wide closure of such areas to support "social distancing" measures (i.e. travels limitation; lockdown of (over)crowd-prone activities). In the "second phase", individual's risk-mitigation strategies (facial masks) could be properly linked to "social distancing" to ensure re-opening touristic cities to visitors. Simulation tools could support the effectiveness evaluation of risk-mitigation measures to look for an economic and social optimum for activities restarting. This work modifies an existing Agent-Based Model to estimate the virus spreading in touristic areas, including tourists and residents' behaviours, movement and virus effects on them according to a probabilistic approach. Consolidated proximity-based and exposure-time-based contagion spreading rules are included according to international health organizations and previous calibration through experimental data. Effects of tourists' capacity (as "social distancing"-based measure) and other strategies (i.e. facial mask implementation) are evaluated depending on virus-related conditions (i.e. initial infector percentages). An idealized scenario representing a significant case study has been analysed to demonstrate the tool capabilities and compare the effectiveness of those solutions. Results show that "social distancing" seems to be more effective at the highest infectors' rates, although represents an extreme measure with important economic effects. This measure loses its full effectiveness (on the community) as the infectors' rate decreases and individuals' protection measures become predominant (facial masks). The model could be integrated to consider other recurring issues on tourist-related fruition and schedule of urban spaces and facilities (e.g. cultural/leisure buildings).

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55×10⁶ Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500×500m² grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Associations between COVID-19 transmission rates, park use, and landscape structure

The COVID-19 pandemic has had severe impacts on global public health. In England, social distancing measures and a nationwide lockdown were introduced to reduce the spread of the virus. Green space accessibility may have been particularly important during this lockdown, as it could have provided benefits for physical and mental wellbeing. However, the associations between public green space use and the rate of COVID-19 transmission are yet to be quantified, and as the size and accessibility of green spaces vary within England's local authorities, the risks and benefits to the public of using green space may be context-dependent. To evaluate how green space affected COVID-19 transmission across 299 local authorities (small regions) in England, we calculated a daily case rate metric, based upon a seven-day moving average, for each day within the period June 1st - November 30th 2020 and assessed how baseline health and mobility variables influenced these rates. Next, looking at the residual case rates, we investigated how landscape structure (e.g. area and patchiness of green space) and park use influenced transmission. We first show that reducing mobility is associated with a decline in case rates, especially in areas with high population clustering. After accounting for known mechanisms behind transmission rates, we found that park use (showing a preference for park mobility) was associated with decreased residual case rates, especially when green space was low and contiguous (not patchy). Our results support that a reduction in overall mobility may be a good strategy for reducing case rates, endorsing the success of lockdown measures. However, if mobility is necessary, outdoor park use may be safer than other forms of mobility and associated activities (e.g. shopping or office-based working).

Associations between COVID-19 transmission rates, park use, and landscape structure

The COVID-19 pandemic has had severe impacts on global public health. In England, social distancing measures and a nationwide lockdown were introduced to reduce the spread of the virus. Green space accessibility may have been particularly important during this lockdown, as it could have provided benefits for physical and mental wellbeing. However, the associations between public green space use and the rate of COVID-19 transmission are yet to be quantified, and as the size and accessibility of green spaces vary within England's local authorities, the risks and benefits to the public of using green space may be context-dependent. To evaluate how green space affected COVID-19 transmission across 299 local authorities (small regions) in England, we calculated a daily case rate metric, based upon a seven-day moving average, for each day within the period June 1st - November 30th 2020 and assessed how baseline health and mobility variables influenced these rates. Next, looking at the residual case rates, we investigated how landscape structure (e.g. area and patchiness of green space) and park use influenced transmission. We first show that reducing mobility is associated with a decline in case rates, especially in areas with high population clustering. After accounting for known mechanisms behind transmission rates, we found that park use (showing a preference for park mobility) was associated with decreased residual case rates, especially when green space was low and contiguous (not patchy). Our results support that a reduction in overall mobility may be a good strategy for reducing case rates, endorsing the success of lockdown measures. However, if mobility is necessary, outdoor park use may be safer than other forms of mobility and associated activities (e.g. shopping or office-based working).

Estimation of the SARS-CoV-2 transmission probability in confined traffic space and evaluation of the mitigation strategies

Public transport is a fundamental service for the resumption of work and production, but the enclosed environment and dense population create very favorable conditions for the spread of epidemic infections. Thus, effective public health interventions are urgently introduced. The objective of this paper is to quantitatively estimate the SARS-CoV-2 transmission probability and evaluate the influence of environmental parameters and individual intervention on the epidemic prevention. For this purpose, (1) we estimate the virus emission rate with Diamond Princess Cruise Ship infection data by Monte Carlo simulation and the improved Wells-Riley model, and (2) employ the reproductive number R to quantify diverse mitigation strategies. Different determinants are examined such as the duration of exposure, the number of passengers combined with individual interventions such as mask type and mask-wearing rate. The results show that the SARS-CoV-2 quantum generation rate is 185.63. The R shows a stronger positive correlation with the exposure time comparing to the number of passengers. In this light, reducing the frequency of long-distance journeys on crowded public transportation may be required to reduce the spread of the virus during the pandemic. N95 mask and surgical mask can reduce the transmission risk by 97 and 84%, respectively, and even homemade mask can reduce the risk by 67%, which indicates that it is necessary to advocate wearing masks on public transportation.

COVID-19: Research Directions for Non-Clinical Aerosol-Generating Facilities in the Built Environment

Physical contact and respiratory droplet transmission have been widely regarded as the main routes of COVID-19 infection. However, mounting evidence has unveiled the risk of aerosol transmission of the virus. Whereas caution has been taken to avoid this risk in association with clinical facilities, facilities such as spa pools and Jacuzzis, which are characterized by bubble-aerosol generation, high bather loads, and limited turnover rates, may promote aerosol transmission. Focusing on these non-clinical facilities in the built environment, a review study was undertaken. First, the typical water disinfection and ventilation-aided operations for the facilities were illustrated. Second, cross comparisons were made between the applicable standards and guidelines of the World Health Organization and countries including Australia, Canada, China, the United Kingdom, and the United States. The similarities and differences in their water quality specifications, ventilation requirements, and air quality enhancement measures were identified; there were no specific regulations for preventing aerosol transmission at those aerosol-generating facilities. Third, a qualitative review of research publications revealed the emergence of studies on potential air-borne transmission of COVID-19, but research on built facilities posing high risks of aerosol transmission remains scant. This study’s results inform key directions for future research on abating aerosol transmission of COVID-19: the development of bespoke personal protective equipment and engineering and management controls on water quality, ventilation, and air quality.

A first insight about spatial dimension of COVID-19: analysis at municipality level

Background

This paper is about spatial patterns of by corona virus disease-2019 (COVID-19).

Methods

Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used.

Results

There are some clusters alongside Catalan municipalities that change over time.

Conclusions

Use of spatial analysis techniques is suggested to identify spatial disease patterns and to provide spatially disaggregated public health policy recommendations.

The novel SARS-CoV-2 pandemic: Possible environmental transmission, detection, persistence and fate during wastewater and water treatment

The contagious SARS-CoV-2 virus, responsible for COVID-19 disease, has infected over 27 million people across the globe within a few months. While literature on SARS-CoV-2 indicates that its transmission may occur predominantly via aerosolization of virus-laden droplets, the possibility of alternate routes of transmission and/or reinfection via the environment requires considerable scientific attention. This review aims to collate information on possible transmission routes of this virus, to ascertain its fate in the environment. Concomitant with the presence of SARS-CoV-2 viral RNA in faeces and saliva of infected patients, studies also indicated its occurrence in raw wastewater, primary sludge and river water. Therefore sewerage system could be a possible route of virus outbreak, a possible tool to assess viral community spread and future surveillance technique. Hence, this review looked into detection, occurrence and fate of SARS-CoV-2 during primary, secondary, and tertiary wastewater and water treatment processes based on published literature on SARS-CoV and other enveloped viruses. The review also highlights the need for focused research on occurrence and fate of SARS-CoV-2 in various environmental matrices. Utilization of this information in environmental transmission models developed for other enveloped and enteric viruses can facilitate risk assessment studies. Preliminary research efforts with SARS-CoV-2 and established scientific reports on other coronaviruses indicate that the threat of virus transmission from the aquatic environment may be currently non-existent. However, the presence of viral RNA in wastewater provides an early warning that highlights the need for effective sewage treatment to prevent a future outbreak of SARS-CoV-2.

Distances of transmission risk of COVID-19 inside dwellings and evaluation of the effectiveness of reciprocal proximity warning sounds

One of the main modes of transmission and propagation of COVID-19 (SARS-CoV-2) is the direct contact with respiratory droplets transmitted among individuals at a certain distance. There are indoor spaces, such as dwellings, in which the transmission risk is high. This research aims to record and analyze risk close contacts in this scope, experimentally assessing the effectiveness of using electronic proximity warning sound devices or systems. For this purpose, the methodology is based on monitoring the location of the occupants of a dwelling. Then, the days in which a proximity warning sound system is installed and activated are compared to the days in which the system is not activated. The results stressed the significant reduction of time and number of close contacts among individuals when the warning was activated. Regarding the relation between the number and the duration of close contacts, together with the reductions mentioned, the possibility of making certain predictions based on the distributions obtained is proved. All this contributes to the progress in the prevention of COVID-19 transmission because of close contacts in dwellings.

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria

University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders' needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible "infectors". While waiting for the vaccine, as for other public buildings, policy-makers' measures to limit virus outbreaks combine individual's strategies (facial masks), occupants' capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main "attraction areas" in the building (classrooms, break-areas). Scenarios are defined in terms of occupants' densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users' comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants' density are thus provided to help stakeholders in organizing users' presences in the building during the pandemic.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

supplementary material is available in the online version of this article at 10.1007/s12273-021-0770-2.

Effects of human behaviour changes during the COVID-19 pandemic on influenza spread in Hong Kong

Background

COVID-19 continues to threaten human life worldwide. We explored how human behaviours have been influenced by the COVID-19 pandemic in Hong Kong, and how the transmission of other respiratory diseases (e.g. influenza) has been influenced by human behaviour.

Methods

We focused on the spread of COVID-19 and influenza infections based on reported COVID-19 cases and influenza surveillance data, and investigated the changes in human behaviour due to COVID-19 based on mass transit railway data and the data from a telephone survey. We did the simulation based on SEIR model to assess the risk reduction of influenza transmission caused by the changes in human behaviour.

Results

During the COVID-19 pandemic, the number of passengers fell by 52.0% compared with the same period in 2019. Residents spent 32.2% more time at home. Each person on average came into close contact with 17.6 and 7.1 people per day during the normal and pandemic periods, respectively. Students, workers, and older people reduced their daily number of close contacts by 83.0%, 48.1%, and 40.3%, respectively. The close contact rates in residences, workplaces, places of study, restaurants, shopping centres, markets, and public transport decreased by 8.3%, 30.8%, 66.0%, 38.5%, 48.6%, 41.0%, and 36.1%, respectively. Based on the simulation, these changes in human behaviours reduced the effective reproduction number of influenza by 63.1%.

Conclusions

Human behaviours were significantly influenced by the COVID-19 pandemic in Hong Kong. Close contact control contributed more than 47% to the reduction in infection risk of COVID-19.

Estimation of the potential spread risk of COVID-19: Occurrence assessment along the Yangtze, Han, and Fu River basins in Hubei, China

Given that the novel coronavirus was detected in stool and urine from diagnosed patients, the potential risk of its transmission through the water environment might not be ignored. In the current study, to investigate the spread possibility of COVID-19 via the environmental media, three typical rivers (Yangtze, Han, and Fu River) and watershed cities in Hubei province of China were selected, and a more comprehensive risk assessment analysis method was built with a risk index proposed. Results showed that the risk index in the Yangtze River Basin is about 10^-12, compared to 10^-10 and 10^-8 in the Han and Fu River Basins, and the risk index is gradually reduced from Wuhan city to the surrounding cities. The safety radius and safety time period for the Yangtze, Han, and Fu River are 8 km/14 h, 20 km/30 h and 36 km/36 h, respectively. The linear relationship between the risk potential calculated by the QMRA model and the multiple linear regression proved that the built index model is statistically significant. By comparing the theoretical removal rates for the novel coronavirus, our study proposed an effective method to estimate the potential spread risk of COVID-19 in the typical river basins.

Impact of intervention methods on COVID-19 transmission in Shenzhen

By March 31, 2020, COVID-19 had spread to more than 200 countries. Over 750,000 confirmed cases were reported, leading to more than 36,000 deaths. In this study, we analysed the efficiency of various intervention strategies to prevent infection by the virus, SARS-CoV-2, using an agent-based SEIIR model, in the fully urbanised city of Shenzhen, Guangdong Province, China. Shortening the duration from symptom onset to hospital admission, quarantining recent arrivals from Hubei Province, and letting symptomatic individuals stay at home were found to be the three most important interventions to reduce the risk of infection in Shenzhen. The ideal time window for a mandatory quarantine of arrivals from Hubei Province was between 10 January and January 17, 2020, while the ideal time window for local intervention strategies was between 15 and 22 January. The risk of infection could have been reduced by 50% if all symptomatic individuals had immediately gone to hospital for isolation, and by 35% if a 14-day quarantine for arrivals from Hubei Province had been introduced one week earlier. Intervention strategies implemented in Shenzhen were effective, and the spread of infection would be controlled even if the initial basic reproduction number had doubled. Our results may be useful for other cities when choosing their intervention strategies to prevent outbreaks of COVID-19.

How the individual human mobility spatio-temporally shapes the disease transmission dynamics

Human mobility plays a crucial role in the temporal and spatial spreading of infectious diseases. During the past few decades, researchers have been extensively investigating how human mobility affects the propagation of diseases. However, the mechanism of human mobility shaping the spread of epidemics is still elusive. Here we examined the impact of human mobility on the infectious disease spread by developing the individual-based SEIR model that incorporates a model of human mobility. We considered the spread of human influenza in two contrasting countries, namely, Belgium and Martinique, as case studies, to assess the specific roles of human mobility on infection propagation. We found that our model can provide a geo-temporal spreading pattern of the epidemics that cannot be captured by a traditional homogenous epidemic model. The disease has a tendency to jump to high populated urban areas before spreading to more rural areas and then subsequently spread to all neighboring locations. This heterogeneous spread of the infection can be captured by the time of the first arrival of the infection \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(T_{fi} )$$\end{document}(Tfi), which relates to the landscape of the human mobility characterized by the relative attractiveness. These findings can provide insights to better understand and forecast the disease spreading.

Close contact behavior in indoor environment and transmission of respiratory infection

Close contact was first identified as the primary route of transmission for most respiratory infections in the early 20th century. In this review, we synthesize the existing understanding of the mechanisms of close contact transmission. We focus on two issues: the mechanism of transmission in close contact, namely the transmission of the expired particles between two people, and the physical parameters of close contact that affect the exposure of particles from one individual to another, or how the nature of close contact plays a role in transmission. We propose the existence of three sub-routes of transmission: short-range airborne, large droplets, and immediate body-surface contact. We also distinguish a "body contact," which is defined with an interpersonal distance of zero, from a close contact. We demonstrate herein that the short-range airborne sub-route may be most common. The timescales over which data should be collected to assess the transmission risk during close contact events are much shorter than those required for the distant airborne or fomite routes. The current paucity of high-resolution data over short distances and timescales makes it very difficult to assess the risk of infection in these circumstances.

A Comparison of Infection Venues of COVID-19 Case Clusters in Northeast China

The world has been suffering from the COVID-19 pandemic since late 2019. In this study, we compared various types of infection locations in which COVID-19 cases clustered, based on the data from three adjacent provinces in Northeast China. The collected data include all officially reported cases in this area until 8 March 2020. We explored the associations between the cases and the frequency of infection locations. The COVID-19 epidemic situation was worse in Heilongjiang Province than in Liaoning and Jilin Provinces. Most clustered cases occurred in individual families and/or between relatives. The transmission in public venues served as a hub for transmitting the disease to other families and results in new clusters. The public transport spread the infection over long distances by transporting infected individuals, and most infections did not seem to occur within vehicles. This field study shows the effect of indoor environments on SARS-CoV-2 transmission and our data may be useful in developing guidance for future disease prevention and control.

Infection Spread and High-Resolution Detection of Close Contact Behaviors

Knowledge of human behaviors is important for improving indoor-environment design, building-energy efficiency, and productivity, and for studies of infection spread. However, such data are lacking. In this study, we designed a device for detecting and recording, second by second, the 3D indoor positioning and head and body motions of each graduate student in an office. From more than 400 person hours of data. Students spent 92.2%, 4.1%, 2.9%, and 0.8% of their time in their own office cubicles, other office cubicles, aisles, and areas near public facilities, respectively. They spent 9.7% of time in close contact, and each student averagely had 4.0 close contacts/h. Students spent long time on close contact in the office which may lead to high infection risk. The average interpersonal distance during close contact was 0.81 m. When sitting, students preferred small relative face orientation angle. Pairs of standing students preferred a face-to-face orientation during close contact which means this pattern had a lower infection risk via close contact. Probability of close contact decreased exponentially with the increasing distance between two students' cubicles. Data on human behaviour during close contact is helpful for infection risk analysis and infection control and prevention.

Increased infection severity in downstream cities in infectious disease transmission and tourists surveillance analysis

Infectious disease severely threatens human life. Human mobility and travel patterns influence the spread of infection between cities and countries. We find that the infection severity in downstream cities during outbreaks is related to transmission rate, recovery rate, travel rate, travel duration and the average number of person-to-person contacts per day. The peak value of the infected population in downstream cities is slightly higher than that in source cities. However, as the number of cities increases, the severity increase percentage during outbreaks between end and source cities is constant. The surveillance of important nodes connecting cities, such as airports and train stations, can help delay the occurrence time of infection outbreaks. The city-entry surveillance of hub cities is not only useful to these cities, but also to cities that are strongly connected (i.e., have a high travel rate) to them. The city-exit surveillance of hub cities contributes to other downstream cities, but only slightly to itself. Surveillance conducted in hub cities is highly efficient in controlling infection transmission. Only strengthening the individual immunity of frequent travellers is not efficient for infection control. However, reducing the number of person-to-person contacts per day effectively limits the spread of infection.

An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model

With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning.

Evaluation and investigation of the effects of ventilation layout, rate, and room temperature on pollution dispersion across a laboratory indoor environment

The presence of chemicals in laboratories and research centers exposes the staff working at such indoor environment to health risks. In this piece of research, a study was performed on the indoor environment of the Center for Environmental Engineering Research at Sahand University of Technology (Tabriz, Iran). For this purpose, the parameters affecting the dispersion of volatile organic compounds (VOCs), including ventilation rate, room temperature, pollution emission time, venting location, air flow regime within the indoor environment, and the number of vents, were simulated via CFD modeling. The CFD modeling was performed three-dimensionally in unsteady state. In case of turbulent flow within the indoor environment, k-ε turbulence model was used to obtain air velocity profile. Experimental data was used to validate the model. Results of the present research showed that when the venting location is on the ceiling, pollution concentration of 25 ppm can be achieved at some low temperature under a particular set of conditions. However, when the venting location was on the walls close to the pollution source, concentrations as low as 5 ppm and lower were observed within the laboratory indoor environment.

Transmission of Influenza A in a Student Office Based on Realistic Person-to-Person Contact and Surface Touch Behaviour

Influenza A viruses result in the deaths of hundreds of thousands of individuals worldwide each year. In this study, influenza A transmission in a graduate student office is simulated via long-range airborne, fomite, and close contact routes based on real data from more than 3500 person-to-person contacts and 127,000 surface touches obtained by video-camera. The long-range airborne, fomite and close contact routes contribute to 54.3%, 4.2% and 44.5% of influenza A infections, respectively. For the fomite route, 59.8%, 38.1% and 2.1% of viruses are transmitted to the hands of students from private surfaces around the infected students, the students themselves and other susceptible students, respectively. The intranasal dose via fomites of the students' bodies, belongings, computers, desks, chairs and public facilities are 8.0%, 6.8%, 13.2%, 57.8%, 9.3% and 4.9%, respectively. The intranasal dose does not monotonously increase or decrease with the virus transfer rate between hands and surfaces. Mask wearing is much more useful than hand washing for control of influenza A in the tested office setting. Regular cleaning of high-touch surfaces, which can reduce the infection risk by 2.14%, is recommended and is much more efficient than hand-washing.

Probable transmission routes of the influenza virus in a nosocomial outbreak

Influenza is a long-standing public health concern, but its transmission remains poorly understood. To have a better knowledge of influenza transmission, we carried out a detailed modelling investigation in a nosocomial influenza outbreak in Hong Kong. We identified three hypothesised transmission modes between index patient and other inpatients based on the long-range airborne and fomite routes. We considered three kinds of healthcare workers’ routine round pathways in 1140 scenarios with various values of important parameters. In each scenario, we used a multi-agent modelling framework to estimate the infection risk for each hypothesis and conducted least-squares fitting to evaluate the hypotheses by comparing the distribution of the infection risk with that of the attack rates. Amongst the hypotheses tested in the 1140 scenarios, the prediction of modes involving the long-range airborne route fit better with the attack rates, and that of the two-route transmission mode had the best fit, with the long-range airborne route contributing about 94% and the fomite route contributing 6% to the infections. Under the assumed conditions, the influenza virus was likely to have spread via a combined long-range airborne and fomite routes, with the former predominant and the latter negligible.