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Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study.
Hu, Maogui; Lin, Hui; Wang, Jinfeng; Xu, Chengdong; Tatem, Andrew J; Meng, Bin; Zhang, Xin; Liu, Yifeng; Wang, Pengda; Wu, Guizhen; Xie, Haiyong; Lai, Shengjie.
  • Hu M; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
  • Lin H; China Academy of Electronics and Information Technology, Beijing, China.
  • Wang J; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
  • Xu C; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
  • Tatem AJ; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom.
  • Meng B; Beijing Union University, Beijing, China.
  • Zhang X; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
  • Liu Y; China Academy of Electronics and Information Technology, Beijing, China.
  • Wang P; China Academy of Electronics and Information Technology, Beijing, China.
  • Wu G; Chinese Center for Disease Control and Prevention, Beijing, China.
  • Xie H; China Academy of Electronics and Information Technology, Beijing, China.
  • Lai S; University of Science and Technology of China, Hefei, China.
Clin Infect Dis ; 72(4): 604-610, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087719
ABSTRACT

BACKGROUND:

Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear.

METHODS:

We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0-8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time.

RESULTS:

The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%-19.0%), with a mean of 0.32% (95% CI, .29%-.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%-1.8%), higher than that in other rows (0.14% [95% CI, .11%-.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6-14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%-4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9-23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered.

CONCLUSIONS:

COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Clin Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article Affiliation country: Cid

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Clin Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article Affiliation country: Cid