Aerosol exchange between pressure-equilibrium rooms induced by door motion and human movement.

It is now widely recognised that aerosol transport is major vector for transmission of diseases such as COVID-19, and quantification of aerosol transport in the built environment is critical to risk analysis and management. Understanding the effects of door motion and human movement on the dispersion of virus-laden aerosols under pressure-equilibrium conditions is of great significance to the evaluation of infection risks and development of mitigation strategies. This study uses novel numerical simulation techniques to quantify the impact of these motions upon aerosol transport and provides valuable insights into the wake dynamics of swinging doors and human movement. The results show that the wake flow of an opening swinging door delays aerosol escape, while that of a person walking out entrains aerosol out of the room. Aerosol escape caused by door motion mainly happens during the closing sequence which pushes the aerosols out. Parametric studies show that while an increased door swinging speed or human movement speed can enhance air exchange across the doorway, the cumulative aerosol exchange across the doorway is not clearly affected by the speeds.

A space-time model for analyzing contagious people based on geolocation data using inverse graphs

Mobile devices provide us with an important source of data that capture spatial movements of individuals and allow us to derive general mobility patterns for a population over time. In this article, we present a mathematical foundation that allows us to harmonize mobile geolocation data using differential geometry and graph theory to identify spatial behavior patterns. In particular, we focus on models programmed using Computer Algebra Systems and based on a space-time model that allows for describing the patterns of contagion through spatial movement patterns. In addition, we show how the approach can be used to develop algorithms for finding ”patient zero” or, respectively, for identifying the selection of candidates that are most likely to be contagious. The approach can be applied by information systems to evaluate data on complex population movements, such as those captured by mobile geolocation data, in a way that analytically identifies, e.g., critical spatial areas, critical temporal segments, and potentially vulnerable individuals with respect to contact events. © 2023 the Author(s), licensee AIMS Press.

Modeling social interaction and metapopulation mobility of the COVID-19 pandemic in main cities of highly populated Java Island, Indonesia: An agent-based modeling approach

IntroductionCoronavirus transmission is strongly influenced by human mobilities and interactions within and between different geographical regions. Human mobility within and between cities is motivated by several factors, including employment, cultural-driven, holidays, and daily routines. MethodWe developed a sustained metapopulation (SAMPAN) model, an agent-based model (ABM) for simulating the effect of individual mobility and interaction behavior on the spreading of COVID-19 viruses across main cities on Java Island, Indonesia. The model considers social classes and social mixing affecting the mobility and interaction behavior within a sub-population of a city in the early pandemic. Travelers' behavior represents the mobility among cities from central cities to other cities and commuting behavior from the surrounding area of each city. ResultsLocal sensitivity analysis using one factor at a time was performed to test the SAMPAN model, and we have identified critical parameters for the model. While validation was carried out for the Jakarta area, we are confident in implementing the model for a larger area with the concept of metapopulation dynamics. We included the area of Bogor, Depok, Bekasi, Bandung, Semarang, Surakarta, Yogyakarta, Surabaya, and Malang cities which have important roles in the COVID-19 pandemic spreading on this island. DiscussionOur SAMPAN model can simulate various waves during the first year of the pandemic caused by various phenomena of large social mobilities and interactions, particularly during religious occasions and long holidays.

Numerical study of transient indoor airflow and virus-laden droplet dispersion: Impact of interactive human movement.

Human movement affects indoor airflow and the airborne transmission of respiratory infectious diseases, which has attracted scholars. However, the interactive airflow between moving and stationary people has yet to be studied in detail. This study used the numerical method validated by experimental data to explore the transient indoor airflow and virus-laden droplet dispersion in scenes with interactive human movement. Human-shaped numerical models and the dynamic mesh method were adopted to realize human movement in scenes with different lateral distances (0.2-1.2 m) between a moving person and stationary (standing/sitting) persons. The interactive human movement obviously impacts other persons' respiratory airflow, and the lateral fusion ranged about 0.6 m. The interactive human movement strengthens the indoor airflow convection, and some exhaled virus-laden droplets were carried into wake flow and enhanced long-range airborne transmission. The impact of interactive human movement on sitting patients' exhalation airflow seems more evident than on standing patients. The impact might last over 2 min after movement stopped, and people in the affected area might be at a higher exposure. The results can provide a reference for epidemic control in indoor environments.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors.

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Prediction of Human Movement in Open Public Spaces: Case Study of Sarajevo

Imposed changes in social conduct and the dynamics of living in cities, during COVID-19 pandemic, triggered an increase in the demand, availability, and accessibility of open public spaces. This has put forward questions of the relationship between open public spaces and disease transmission, as well as how planning and design strategies might be used to improve resilience in the face of future pandemics. Within this academic framework, this study focuses on object detection and human movement prediction in open public spaces, using the city of Sarajevo as a case study. Video recordings of parks and squares in morning, afternoon and evening are utilized to detect humans and predict their movements. Frame differentiation method proved to be the best for object detection and their motion. Linear regression is used on a dataset collected using the space syntax observation technique gate method. The best R-2 values, 0.97 and 0.61, are achieved for weekdays, for both parks and squares. Authors associated it with the dynamics of space use and frequency of space occupancy, which can be related to physical conditions and activity content of selected locations. The results of study provide an insight into analysis and prediction of direction, as well as density of pedestrian movement, which could be used in decision making directed towards more efficient and health oriented urban planning.

Effect of human movement on a patient's exhaled viral particle transmission: A numerical study

Droplet transmission and aerosol transmission are both possible transmission pathways for many respiratory infections (e.g., COVID-19) and human movements may affect these viral particle transmission pathways. Realistic 3-D human models were used here in a computational fluid dynamics (CFD) study to analyze the effect of human movements on the transmission of virus particles exhaled by a patient. The changes in the airflow, pressure and particle diffusion were compared with experimental data to verify the accuracy of the computations. The results show that when a person passes by a sitting patient in a poorly ventilated room, the wake velocities can reach 1.6~2.0 m/s. The airflow velocity can reach 0.53 m/s at 0.10 m from the moving person, 0.22 m/s at 0.25 m away, and 0.13 m/s at 0.55 m away. The airflow fluctuations can last more than 10 s. Double peak airflow velocities are found near the moving person. The pressure difference of 0.49 Pa caused by the moving person moves the air and the viral particles into the wake of the moving person and slows the nearby droplet deposition. More than 50% of the viral particles are deposited on the moving person's body or spread further. Thus, this study recommends less cross-area movement in epidemic areas and that all people should wear masks and use personalized ventilation equipment. © 2022, Tsinghua University Press. All right reserved.

Assessing the potential repercussions of the COVID-19 pandemic on global SDG attainment.

The coronavirus disease (COVID-19) pandemic has led to a worldwide lockdown, and this restriction on human movements and activities has significantly affected society and the environment. Some effects might be quantitative, but some might be qualitative, and some effects could prolong immediately and/or persistently. This study examined the consequences of global lockdown for human movement and nitrogen dioxide (NO2) emissions using an air pollution index and dataset and satellite image analyses. We also evaluated the immediate (during lockdown) and persistent (after lockdown) effects of lockdown on achieving the SDGs. Our analysis revealed a drastic reduction in human movement and NO2 emissions and showed that many SDGs were influenced both immediately and persistently due to the global lockdown. We observed the immediate negative impacts on four goals and positive impacts on five goals, especially those concerning economic issues and ecosystem conservation, respectively. The persistent effects of lockdown were likely to be predominantly reversed from their immediate impacts due to economic recovery. The global lockdown has influenced the global community's ability to meet the SDGs, and our analysis provides powerful insights into the status of the internationally agreed-upon SDGs both during and after the COVID-19-induced global lockdown. Supplementary Information: The online version contains supplementary material available at 10.1007/s43621-021-00067-2.

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

Transient and continuous effects of indoor human movement on nanoparticle concentrations in a sitting person's breathing zone.

Particle concentration in a sitting person's breathing zone can be influenced by human movement around the person, and the transient and continuous effects may differ. In this study, a set of full-scale experiments was conducted to sample the nanoparticle concentration in the breathing zone of a sitting thermal breathing manikin (STBM). The transient fluctuation of the nanoparticle concentration was recorded continuously and analyzed. The results showed that when a manikin moved (at 1 m/s) past the STBM, the nanoparticle concentration in the STBM's breathing zone decreased and reached its lowest after the standing manikin had passed, decreasing 37.6 (±5.7) % compared with the peak value. The average concentration in the STBM's breathing zone during influence periods was 5.18 (±0.99) % less than that during non-influence Periods (NP). This finding reflected the fact that the transient inhalation (over several seconds) of the STBM may be reduced by manikin movement. On the other hand, the exposure of the STBM increased 2.88 (±1.24) % when there was a continuously moving manikin compared with the stable state in a 10-min observation. This finding may be explained by the fuller mix of indoor air and nanoparticles caused by manikin movement, as well as the increase of nanoparticle suspension time. The difference in the transient and continuous effects of the manikin movement on the STBM's exposure shows the importance of considering these effects separately in different scenarios.

A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality.

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK.

The impact and implications of COVID-19: Reflections on the Zimbabwean society.

The article is an attempt to provide a kaleidoscopic interpretation of how social science scholarship views the socio-cultural terrain of Zimbabwe during and after the global health crisis, and the societal and business haemorrhage induced by the coronavirus (COVID-19). Built through a multi-perspective and triangulation involving a modified Delphic approach that engages archival methods involving document and literature review, content analysis and expert interpretation; the article unveils the various effects of COVID-19 on Zimbabwe. It is concluded that COVID-19 by its nature is disruptive to everyday life, restrictive to human-social relations and is an instigator to tradition, spirituality and intellectuality in the country. The challenge of the virus brings to society a deliberate consciousness that global processes and events are converging (borders are porous) while local embeddedness is being entrenched through practices like lockdowns and confinement.

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.

BACKGROUND: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). OBJECTIVE: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). METHODS: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. RESULTS: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. CONCLUSIONS: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24432.