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

Mo, Baichuan; Feng, Kairui; Shen, Yu; Tam, Clarence C.; Li, Daqing; Yin, Yafeng; Zhao, Jinhua

doi:10.1016/j.trc.2020.102893

Cited by 82 publications

(59 citation statements)

References 52 publications

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“…An analysis in New York City showed no association between subway ridership and COVID-19 infection rates . Modeling of Singapore's busy bus system (3:9 million riders daily in a population of 5:7 million) showed that certain interventions-especially universal mask-wearing and isolation of super-spreaders identified through the use of smart cards-could greatly limit disease transmission (Mo et al 2021). However, the use of transit relates as much to public confidence as to objective data.…”

Section: Changing Modes Of Travel?mentioning

confidence: 99%

COVID-19, the Built Environment, and Health

Frumkin

2021

Environ Health Perspect

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BACKGROUND: Since the dawn of cities, the built environment has both affected infectious disease transmission and evolved in response to infectious diseases. COVID-19 illustrates both dynamics. The pandemic presented an opportunity to implement health promotion and disease prevention strategies in numerous elements of the built environment. OBJECTIVES: This commentary aims to identify features of the built environment that affect the risk of COVID-19 as well as to identify elements of the pandemic response with implications for the built environment (and, therefore, for long-term public health). DISCUSSION: Built environment risk factors for COVID-19 transmission include crowding, poverty, and racism (as they manifest in housing and neighborhood features), poor indoor air circulation, and ambient air pollution. Potential long-term implications of COVID-19 for the built environment include changes in building design, increased teleworking, reconfigured streets, changing modes of travel, provision of parks and greenspace, and population shifts out of urban centers. Although it is too early to predict with confidence which of these responses may persist, identifying and monitoring them can help health professionals, architects, urban planners, and decision makers, as well as members of the public, optimize healthy built environments during and after recovery from the pandemic.

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Section: Changing Modes Of Travel?mentioning

confidence: 99%

COVID-19, the Built Environment, and Health

Frumkin

2021

Environ Health Perspect

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“…A normal degree distribution was observed in this network. Previous studies have shown an exponential degree distribution for passenger contact network created for a city-wide transit system (1,7,13). After running the community detection algorithm, three different communities were found.…”

Section: Passenger Contact Network Resultsmentioning

confidence: 97%

“…One of the major contributing factors in an outbreak is the spread of a disease through the movement of people from one location to another. This includes movement of people through air transportation network, road network, transit network, and so on ( 1 , 5 , 7 , 12 , 13 , 15 ). The air transportation network is responsible for carrying a disease globally while road and transit networks are responsible for the local transmission of disease.…”

Section: Related Workmentioning

confidence: 99%

“…Conversely, automated data collection systems (ADCS)—namely, automatic fare collection (AFC) system, automatic passenger count (APC) system, and automatic vehicle location (AVL) system—which are designed for administrative purposes such as operations management and service planning, provide a continuous source of information about passenger travel patterns ( 9 – 11 ). Previous studies have successfully used smart card AFC data to understand passenger encounters in a public transit system ( 1 , 7 , 12 , 13 ). However, the number of passenger encounters computed using this data can be underestimated depending on the penetration of smart card use among travelers.…”

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confidence: 99%

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Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data

Kumar

Khani

Lind

et al. 2021

Transportation Research Record: Journal of the Transportation R

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This paper studies the potential spread of infectious disease through passenger encounters in a public transit system using automatic passenger count (APC) data. An algorithmic procedure is proposed to evaluate three different measures to quantify these encounters. The first two measures quantify the increased possibility of disease spread from passenger interaction when traveling between different origin–destination pairs. The third measure evaluates an aggregate measure quantifying the relative risk of boarding at a particular stop of the transit route. For calculating these measures, compressed sensing is employed to estimate a sparse passenger flow matrix planted in the underdetermined system of equations obtained from the APC data. Using the APC data of Route 5 in Minneapolis/St. Paul region during the COVID-19 pandemic, it was found that all three measures grow abruptly with the number of passengers on board. The passenger contact network is densely connected, which further increases the potential risk of disease transmission. To reduce the relative risk, it is proposed to restrict the number of passengers on-board and analyze the effect of this using a simulation framework. It was found that a considerable reduction in the relative risk can be achieved when the maximum number of passengers on-board is restricted below 15. To account for the reduced capacity and still maintain reasonable passenger wait times, it would then be necessary to increase the frequency of the route.

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“…The findings of the models can only at best demonstrate that transmission is possible on public transport; the calculated transmission rates depend heavily on assumptions of infection dynamics within the population, alongside assumptions of the demand and operational characteristics of public transport. Mo et al (2021) use a susceptible-exposed-infectious-removed (SEIR) model coupled with an encounter network model of contacts to estimate infection rates on the Singapore bus network using demand data for regular operations in 2014. The authors report that to reduce R below 1 (i.e.…”

Section: Existing Evidence and Early Policy Responsementioning

confidence: 99%