Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2021 Jan 8:2021.01.02.21249119.
doi: 10.1101/2021.01.02.21249119.

Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

Chengbo Zeng  1   2   3 , Jiajia Zhang  1   3   4 , Zhenlong Li  1   3   5 , Xiaowen Sun  1   3   4 , Bankole Olatosi  1   3   6 , Sharon Weissman  3   7 , Xiaoming Li  1   2   3
Affiliations

Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

Chengbo Zeng et al. medRxiv. .

Update in

Abstract

Background: Population mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19.

Objective: To examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC.

Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals.

Results: Population mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3-, 7-, 14- days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9-, 14-, 28-, 20-, and 9- days, respectively. The 14-day prediction accuracy ranged from 60.3% to 74.5%.

Conclusions: Population mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest

None declared.

Figures

Figure 1.
Figure 1.
Daily COVID-19 new cases at both state- and county-levels in SC Note: SC: South Carolina.
Figure 2.
Figure 2.
Daily population mobility at both state- and county-levels in SC Note: SC: South Carolina.
See this image and copyright information in PMC

References

    1. Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19): Cases & Deaths in the US. 2020. Retrived from: https://www.cdc.gov/coronavirus/2019-ncov/index.html
    1. Bohatch E. SC sees more than 2,200 coronavirus cases, second highest increase in one day. The State. 2020. Retrived from: https://www.thestate.com/news/coronavirus/article244215817.html
    1. South Carolina Department of Health and Environmental Control. SC Demographic Data (COVID-19). 2020. Retrived from: https://www.scdhec.gov/infectious-diseases/viruses/coronavirus-disease-2...
    1. Drake TM, Docherty AB, Weiser TG, Yule S, Sheikh A, Harrison EM. The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK. Lancet Digit Health. 2020;2(8):e385–e387. doi: 10.1016/S2589-7500(20)30134-5 - DOI - PMC - PubMed
    1. Wang S, Liu Y, Hu T. Examining the change of human mobility adherent to social restriction policies and its effect on COVID-19 cases in Australia. Int J Environ Res Public Health. 2020;17(21). doi: 10.3390/ijerph17217930 - DOI - PMC - PubMed

Publication types

LinkOut - more resources