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Article ; Online: Analyzing the Trends of COVID-19 and Human Activity Intensity in Malaysia.

Chin, Wei Chien Benny / Chan, Chun-Hsiang

Tropical medicine and infectious disease

2023 Volume 8, Issue 2

Abstract: COVID-19 has struck the world with multiple waves. Each wave was caused by a variant and presented different peaks and baselines. This made the identification of waves with the time series of the cases a difficult task. Human activity intensities may ... ...

Abstract	COVID-19 has struck the world with multiple waves. Each wave was caused by a variant and presented different peaks and baselines. This made the identification of waves with the time series of the cases a difficult task. Human activity intensities may affect the occurrence of an outbreak. We demonstrated a metric of time series, namely log-moving-average-ratio (LMAR), to identify the waves and directions of the changes in the disease cases and check-ins (MySejahtera). Based on the detected waves and changes, we explore the relationship between the two. Using the stimulus-organism-response model with our results, we presented a four-stage model: (1) government-imposed movement restrictions, (2) revenge travel, (3) self-imposed movement reduction, and (4) the new normal. The inverse patterns between check-ins and pandemic waves suggested that the self-imposed movement reduction would naturally happen and would be sufficient for a smaller epidemic wave. People may spontaneously be aware of the severity of epidemic situations and take appropriate disease prevention measures to reduce the risks of exposure and infection. In summary, LMAR is more sensitive to the waves and could be adopted to characterize the association between travel willingness and confirmed disease cases.
Language	English
Publishing date	2023-01-19
Publishing country	Switzerland
Document type	Journal Article
ISSN	2414-6366
ISSN (online)	2414-6366
DOI	10.3390/tropicalmed8020072
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Effects of the interplay between topology and function of an integrated urban development on patterns of user movement.

Manivannan, Ajaykumar / Chin, Wei Chien Benny / Gopalakrishnan, Srilalitha / Wong, Daniel K H / Schroepfer, Thomas / Bouffanais, Roland

Scientific reports

2024 Volume 14, Issue 1, Page(s) 7021

Abstract: With the advent of distributed multi-sensory networks of devices, vast troves of real-time data can be gathered about our interactions with the built environment. These rich data sets can be mined to achieve improved and informed data-driven designs of ... ...

Abstract	With the advent of distributed multi-sensory networks of devices, vast troves of real-time data can be gathered about our interactions with the built environment. These rich data sets can be mined to achieve improved and informed data-driven designs of buildings, neighborhoods, and potentially entire cities. Among those, integrated developments have the peculiarity of combining multiple functions within a compact space and, as such, behave as microcosms of a city that can help address the problem of urban sprawl and density. However, a general lack of data and framework about integrated developments hinders our ability to test design hypotheses about the complex interplay between heterogeneity in both space and function. Here, we apply a data-driven approach to analyze the joint influence of topology and function on user movement within a state-of-the-art integrated development in Singapore. Specifically, we leverage the network representation of the building and use movement data collected from 51 individuals over a month. We show evidence of correlation (40%) between the spatial network features and human movement at the building level. We are also able to quantify the relationship between the functional and spatial components of the integrated development through user movement. Previous studies have shown a 60% or higher correlation between the topology and human movement at the city or country scales. Our moderate correlation, therefore, implies that more factors influencing user movement are at play. The heterogeneity in the spatial function introduced trips with diverse origins and destinations. A further data-driven analysis integrating origins and destinations reveals both qualitative and quantitative means of studying the relationship between the built environment and the processes that take place in them.
Language	English
Publishing date	2024-03-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-57475-3
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Article ; Online: Spatial super-spreaders and super-susceptibles in human movement networks.

Chin, Wei Chien Benny / Bouffanais, Roland

Scientific reports

2020 Volume 10, Issue 1, Page(s) 18642

Abstract: As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public ... ...

Abstract	As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be "super-spreaders". Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/transmission ; COVID-19/virology ; Carrier State ; Humans ; Movement ; SARS-CoV-2/isolation & purification ; Singapore/epidemiology ; Social Isolation ; Transportation
Keywords	covid19
Language	English
Publishing date	2020-10-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-75697-z
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Article ; Online: Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore

Chien Benny Chin, Wei / Feng, Chen-Chieh / Leong, Chan-Hoong / Pang, Junxiong / Eleanor Clapham, Hannah / Nara, Atsushi / Tsou, Ming-hsiang / Wang, Yi-Chen

International Journal of Applied Earth Observation and Geoinformation. 2023, p.103376-

2023 , Page(s) 103376–

Abstract: Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity ... ...

Abstract	Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead to the transition of vulnerability from locations to locations. This study presented an analytical framework based on the Particle Swarm Optimization model to estimate the weights of the factors for vulnerability modeling, and a local proportional parameter for use in the integration of the local and neighboring area risks. A country model and five cross-region validation models were developed for the case study of Singapore to assess the vulnerability to COVID-19. The results showed that the identified weights for the factors were robust throughout the optimization process and across various models. The local proportional parameter could be set slightly higher in between 0.6 and 0.8 (out of 1), signifying that the local effect was higher than the neighboring effect. Computation of the weights from the optimal solutions for the integrated vulnerability index showed that the factors of human activity intensity and accessibility to amenities had much higher weights, at 0.5 and 0.3, respectively. Conversely, the weights of population density, elderly population, social economic status and land use diversity were much lower. These findings underscored the importance of considering non-equal weights for factors and incorporating spatial interactions between local and neighboring areas for vulnerability modeling, to provide to a more comprehensive assessment of vulnerability to infectious diseases.
Keywords	COVID-19 infection ; Singapore ; algorithms ; case studies ; elderly ; humans ; land use ; models ; population density ; risk assessment ; socioeconomic status ; spatial data ; vulnerability ; disease risk ; spatial big data ; Particle Swarm Optimization ; human dynamics
Language	English
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Pre-press version ; Use and reproduction
ISSN	1569-8432
DOI	10.1016/j.jag.2023.103376
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: The effect of water sound level in virtual reality: A study of restorative benefits in young adults through immersive natural environments

Hsieh, Chung-Heng / Yang, Ju-Yuan / Huang, Junwei / Chin, Wei Chien Benny

Journal of Environmental Psychology. 2023, p.102012-

2023 , Page(s) 102012–

Abstract: Virtual Reality (VR) can bring a person to a restorative natural environment-an immersive experience containing elements from blue and green space that help reduce stress and anxiety. However, the effect of different water sound levels on psychological ... ...

Abstract	Virtual Reality (VR) can bring a person to a restorative natural environment-an immersive experience containing elements from blue and green space that help reduce stress and anxiety. However, the effect of different water sound levels on psychological and physiological health remains unclear. This study used a VR natural environment-combining forest scene and waterfall sounds-to compare the low and high water sound levels' effects on individuals' psychological (affective quality of place and state anxiety relief) and physiological benefits (heart rate variability). We focused on two comparisons in each of the two dimensions: (1) a before-and-after comparison that analyzes the effectiveness of the water sounds, and (2) a low-and-high comparison that investigates the differences between the two sound levels. A total of 45 university students (aged between 19 and 23) participated in the experiment. The results show that both low- and high-decibel levels improved in psychological dimension, and decrease individuals' heart rates from anxiety status. More importantly, while the high-decibel served to awaken emotions, the low-decibel was more effective at calming emotions and relieving anxiety. Overall, we found that natural waterscape sounds relieve transient anxiety states and promote healthy autonomic nervous activity. This study provides evidence for the planning of healthy environmental interaction patterns and the design of urban soundscapes.
Keywords	anxiety ; computer simulation ; forests ; green infrastructure ; heart ; heart rate ; Water soundscape ; Virtual reality ; Restorative environment ; Sound levels
Language	English
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Pre-press version
ISSN	0272-4944
DOI	10.1016/j.jenvp.2023.102012
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Spatial super-spreaders and super-susceptibles in human movement networks

Wei Chien Benny Chin / Roland Bouffanais

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 19

Abstract: Abstract As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public ... ...

Abstract	Abstract As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be “super-spreaders”. Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.
Keywords	Medicine ; R ; Science ; Q
Subject code	380
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Spatial super-spreaders and super-susceptibles in human movement networks

Chin, Wei Chien Benny / Bouffanais, Roland

Abstract	As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be"super-spreaders."Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: Spatial super-spreaders and super-susceptibles in human movement networks

Chin, Wei Chien Benny / Bouffanais, Roland

2020

Abstract	As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be "super-spreaders." Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness. Comment: 19 pages, 10 figures
Keywords	Physics - Physics and Society ; Computer Science - Social and Information Networks
Subject code	380
Publishing date	2020-05-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: On the Challenges and Potential of Using Barometric Sensors to Track Human Activity.

Manivannan, Ajaykumar / Chin, Wei Chien Benny / Barrat, Alain / Bouffanais, Roland

Sensors (Basel, Switzerland)

2020 Volume 20, Issue 23

Abstract: Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of ... ...

Abstract	Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of microelectromechanical system (MEMS)-based barometers and their systematic embedding in smartphones and wearable devices, a vast breadth of new applications for the use of barometers has emerged. For instance, it is now possible to use barometers in conjunction with other sensors to track and identify a wide range of human activity classes. However, the effectiveness of barometers in the growing field of human activity recognition critically hinges on our understanding of the numerous factors affecting the atmospheric pressure, as well as on the properties of the sensor itself-sensitivity, accuracy, variability, etc. This review article thoroughly details all these factors and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition. In addition, we specifically collected some experimental data to illustrate the effects of these factors, which we observed to be in good agreement with the findings in the literature. We conclude this review with some suggestions on some possible future uses of barometric sensors for the specific purpose of tracking human activities.
MeSH term(s)	Human Activities ; Humans ; Micro-Electrical-Mechanical Systems ; Monitoring, Physiologic ; Smartphone ; Wearable Electronic Devices
Language	English
Publishing date	2020-11-27
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s20236786
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Article ; Online: Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore

Wei Chien Benny Chin / Chen-Chieh Feng / Chan-Hoong Leong / Junxiong Pang / Hannah Eleanor Clapham / Atsushi Nara / Ming-Hsiang Tsou / Yi-Chen Wang

International Journal of Applied Earth Observations and Geoinformation, Vol 121, Iss , Pp 103376- (2023)

2023

Abstract	Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead to the transition of vulnerability from locations to locations. This study presented an analytical framework based on the Particle Swarm Optimization model to estimate the weights of the factors for vulnerability modeling, and a local proportional parameter for use in the integration of the local and neighboring area risks. A country model and five cross-region validation models were developed for the case study of Singapore to assess the vulnerability to COVID-19. The results showed that the identified weights for the factors were robust throughout the optimization process and across various models. The local proportional parameter could be set slightly higher in between 0.6 and 0.8 (out of 1), signifying that the local effect was higher than the neighboring effect. Computation of the weights from the optimal solutions for the integrated vulnerability index showed that the factors of human activity intensity and accessibility to amenities had much higher weights, at 0.5 and 0.3, respectively. Conversely, the weights of population density, elderly population, social economic status and land use diversity were much lower. These findings underscored the importance of considering non-equal weights for factors and incorporating spatial interactions between local and neighboring areas for vulnerability modeling, to provide to a more comprehensive assessment of vulnerability to infectious diseases.
Keywords	Vulnerability ; Disease risk ; Spatial big data ; Particle Swarm Optimization ; Human dynamics ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
Subject code	910
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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