Modeling epidemic spreading through public transit using time-varying encounter network
Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global lev...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
|
| Online Access: | https://hdl.handle.net/1721.1/129678 |
| _version_ | 1826201926584238080 |
|---|---|
| author | Mo, Baichuan Feng, Kairui Shen, Yu Tam, Clarence Li, Daqing Yin, Yafeng Zhao, Jinhua |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Mo, Baichuan Feng, Kairui Shen, Yu Tam, Clarence Li, Daqing Yin, Yafeng Zhao, Jinhua |
| author_sort | Mo, Baichuan |
| collection | MIT |
| description | Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying “influential passengers” using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading. |
| first_indexed | 2024-09-23T11:59:02Z |
| format | Article |
| id | mit-1721.1/129678 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:59:02Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1296782023-01-04T04:51:21Z Modeling epidemic spreading through public transit using time-varying encounter network Mo, Baichuan Feng, Kairui Shen, Yu Tam, Clarence Li, Daqing Yin, Yafeng Zhao, Jinhua Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying “influential passengers” using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading. 2021-02-04T17:25:11Z 2021-02-04T17:25:11Z 2021-01 2020-10 2021-02-04T13:13:36Z Article http://purl.org/eprint/type/JournalArticle 0968-090X https://hdl.handle.net/1721.1/129678 Mo, Baichuan et al. "Modeling epidemic spreading through public transit using time-varying encounter network." Transportation Research Part C: Emerging Technologies 122 (January 2021): 102893 © 2020 Elsevier Ltd en http://dx.doi.org/10.1016/j.trc.2020.102893 Transportation Research Part C: Emerging Technologies Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV arXiv |
| spellingShingle | Mo, Baichuan Feng, Kairui Shen, Yu Tam, Clarence Li, Daqing Yin, Yafeng Zhao, Jinhua Modeling epidemic spreading through public transit using time-varying encounter network |
| title | Modeling epidemic spreading through public transit using time-varying encounter network |
| title_full | Modeling epidemic spreading through public transit using time-varying encounter network |
| title_fullStr | Modeling epidemic spreading through public transit using time-varying encounter network |
| title_full_unstemmed | Modeling epidemic spreading through public transit using time-varying encounter network |
| title_short | Modeling epidemic spreading through public transit using time-varying encounter network |
| title_sort | modeling epidemic spreading through public transit using time varying encounter network |
| url | https://hdl.handle.net/1721.1/129678 |
| work_keys_str_mv | AT mobaichuan modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT fengkairui modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT shenyu modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT tamclarence modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT lidaqing modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT yinyafeng modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork AT zhaojinhua modelingepidemicspreadingthroughpublictransitusingtimevaryingencounternetwork |