Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale

Abstract

As of February 27, 2020, 82,294 confirmed cases of coronavirus disease (COVID-19) have been reported since December 2019, including 2,804 deaths, with cases reported throughout China, as well as in 45 international locations outside of mainland China. We predict the spatiotemporal spread of reported COVID- 19 cases at the global level during the first few weeks of the current outbreak by analyzing openly available geolocated Twitter social media data. Human mobility patterns were estimated by analyzing geolocated 2013–2015 Twitter data from users who had: i) tweeted at least twice on consecutive days from Wuhan, China, between November 1, 2013, and January 28, 2014, and November 1, 2014, and January 28, 2015; and ii) left Wuhan following their second tweet during the time period under investigation. Publicly available COVID-19 case data were used to investigate the correlation among cases reported during the current outbreak, locations visited by the study cohort of Twitter users, and airports with scheduled flights from Wuhan. Infectious Disease Vulnerability Index (IDVI) data were obtained to identify the capacity of countries receiving travellers from Wuhan to respond to COVID-19. Our study cohort comprised 161 users. Of these users, 133 (82.6%) posted tweets from 157 Chinese cities (1,344 tweets) during the 30 days after leaving Wuhan following their second tweet, with a median of 2 (IQR= 1–3) locations visited and a mean distance of 601 km (IQR= 295.2–834.7 km) traveled. Of our user cohort, 60 (37.2%) traveled abroad to 119 locations in 28 countries. Of the 82 COVID-19 cases reported outside China as of January 30, 2020, 54 cases had known geolocation coordinates and 74.1% (40 cases) were reported less than 15 km (median = 7.4 km, IQR= 2.9–285.5 km) from a location visited by at least one of our study cohort’s users. Countries visited by the cohort’s users and which have cases reported by January 30, 2020, had a median IDVI equal to 0.74. We show that social media data can be used to predict the spatiotemporal spread of infectious diseases such as COVID-19. Based on our analyses, we anticipate cases to be reported in Saudi Arabia and Indonesia; additionally, countries with a moderate to low IDVI (i.e. ≤0.7) such as Indonesia, Pakistan, and Turkey should be on high alert and develop COVID- 19 response plans as soon as permitting.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

References

WHO. Novel coronavirus – China. Jan 12, 2020. http://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/ (accessed April 20, 2020).

Su S, Wong G, Shi W, et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol 2016; 24:490-502. DOI: https://doi.org/10.1016/j.tim.2016.03.003

WHO. Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003. Dec 31, 2003. https://www.who.int/csr/sars/country/table2004_04_21/en/ (accessed April 20, 2020).

WHO. Middle East respiratory syndrome coronavirus (MERS-CoV). November, 2019. http://www.who.int/emergencies/mers-cov/en/ (accessed April 20, 2020).

Chan JF, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020 Jan 24. pii: S0140-6736(20)30154-9.

Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497-506 https://doi.org/10.1016/S0140-6736(20)30183-5 (accessed April 20, 2020) DOI: https://doi.org/10.1016/S0140-6736(20)30183-5

Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA 2020; 323:1239-1242. https://doi:10.1001/jama.2020.2648 (accessed April 20, 2020) DOI: https://doi.org/10.1001/jama.2020.2648

https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov) (accessed April 20, 2020)

WHO. Novel Coronavirus(2019-nCoV) Situation Report – 10. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200130-sitrep-10-ncov.pdf?sfvrsn=d0b2e480_2 (accessed February 27, 2020).

http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337/3

Findlater A, Bogoch, II. Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel. Trends Parasitol. 2018; 34: 772-83. DOI: https://doi.org/10.1016/j.pt.2018.07.004

Moore M, Gelfeld B, Okunogbe A, Paul C. Identifying future disease hot spots: infectious disease vulnerability index. Rand Health Quarterly 2017; 6: . DOI: https://doi.org/10.7249/RR1605

Kraemer, MUG, Bisanzio D, Reiner RC, et al. Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan. EPJ Data Science 2018; 7: 16. DOI: https://doi.org/10.1140/epjds/s13688-018-0144-x

Schneider CM, Belik V, Couronnné, E Smoreda Z, González MC. Unraveling Daily Human Mobility Motifs. J R Soc Interface 2013; 10: 20130246. DOI: https://doi.org/10.1098/rsif.2013.0246

Lenormand M, Picornell M, Cantú-Ros OG, et al. Cross-checking different sources of mobility information. PloS One 2014; 9: e105184. DOI: https://doi.org/10.1371/journal.pone.0105184

Lai S, Bogoch II, Watts A, Khan K, Li Z, Tatem A. Preliminary risk analysis of 2019 novel coronavirus spread within and beyond China. https://www.worldpop.org/resources/docs/china/WorldPop-coronavirus-spread-risk-analysis-v1-25Jan.pdf (accessed April 20, 2020)

https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov) (accessed April 20, 2020)

WHO. Coronavirus disease 2019 (COVID-19). April 20, 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200420-sitrep-91-covid-19.pdf?sfvrsn=fcf0670b_4 (accessed April 20, 2020).

Published
2020-06-15
Info
Issue
Section
Original Articles
Keywords:
SARS-CoV-2, COVID-19, Epidemiology, Twitter, Mobility
Statistics
  • Abstract views: 2040

  • PDF: 968
  • HTML: 0
How to Cite
Bisanzio, D., Kraemer, M. U., Bogoch, I. I., Brewer, T., Brownstein, J. S., & Reithinger, R. (2020). Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.882