IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i18p6748-d1238578.html
   My bibliography  Save this article

Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota

Author

Listed:
  • Ahmed Nahian

    (Division of Basic Biomedical Sciences, University of South Dakota, 414 E. Clark St., Vermillion, SD 57069, USA)

  • Victor C. Huber

    (Division of Basic Biomedical Sciences, University of South Dakota, 414 E. Clark St., Vermillion, SD 57069, USA)

  • Lisa M. McFadden

    (Division of Basic Biomedical Sciences, University of South Dakota, 414 E. Clark St., Vermillion, SD 57069, USA)

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which is the source of the coronavirus disease 2019 (COVID-19), was declared a pandemic in the March of 2020. Travel and tourism were severely impacted as restrictions were imposed to help slow the disease spread, but some states took alternative approaches to travel restrictions. This study investigated the spread of COVID-19 in South Dakota during the early pandemic period to better understand how tourism affected the movement of the virus within the region. Sequences from the fall of 2020 were retrieved from public sources. CDC and other sources were used to determine infections, deaths, and tourism metrics during this time. The data were analyzed using correlation and logistic regression. This study found that the number of unique variants per month was positively correlated with hotel occupancy, but not with the number of cases or deaths. Interestingly, the emergence of the B.1.2 variant in South Dakota was positively correlated with increased case numbers and deaths. Data show that states with a shelter-in-place order were associated with a slower emergence of the B.1.2 variant compared to states without such an order, including South Dakota. Findings suggest complex relationships between tourism, SARS-CoV-2 infections, and mitigation strategies. The unique approach that South Dakota adopted provided insights into the spread of the disease in areas without state-wide restrictions. Our results suggest both positive and negative aspects of this approach. Finally, our data highlight the need for future surveillance efforts, including efforts focused on identifying variants with known increased transmission potential to produce effective population health management.

Suggested Citation

  • Ahmed Nahian & Victor C. Huber & Lisa M. McFadden, 2023. "Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota," IJERPH, MDPI, vol. 20(18), pages 1-11, September.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:18:p:6748-:d:1238578
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/18/6748/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/18/6748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bart Roelofs & Dimitris Ballas & Hinke Haisma & Arjen Edzes, 2022. "Spatial mobility patterns and COVID‐19 incidence: A regional analysis of the second wave in the Netherlands," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S1), pages 21-40, November.
    2. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    3. Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Lei Che & Jiangang Xu & Hong Chen & Dongqi Sun & Bao Wang & Yunuo Zheng & Xuedi Yang & Zhongren Peng, 2022. "Evaluation of the Spatial Effect of Network Resilience in the Yangtze River Delta: An Integrated Framework for Regional Collaboration and Governance under Disruption," Land, MDPI, vol. 11(8), pages 1-20, August.
    5. Andrew J. Curtis & Jayakrishnan Ajayakumar & Jacqueline Curtis & Sam Brown, 2022. "Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
    6. Liu, Li-Jing & Yao, Yun-Fei & Liang, Qiao-Mei & Qian, Xiang-Yan & Xu, Chun-Lei & Wei, Si-Yi & Creutzig, Felix & Wei, Yi-Ming, 2021. "Combining economic recovery with climate change mitigation: A global evaluation of financial instruments," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 438-453.
    7. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    8. Qi Yan & Siqing Shan & Menghan Sun & Feng Zhao & Yangzi Yang & Yinong Li, 2022. "A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area," IJERPH, MDPI, vol. 19(13), pages 1-16, July.
    9. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    10. Yin Huang & Runda Liu & Shumin Huang & Gege Yang & Xiaofan Zhang & Yin Qin & Lisha Mao & Sishi Sheng & Biao Huang, 2021. "Imbalance and breakout in the post-epidemic era: Research into the spatial patterns of freight demand network in six provinces of central China," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    11. Fu, Xin & Qiang, Yongjie & Liu, Xuxu & Jiang, Ying & Cui, Zhiwei & Zhang, Deyu & Wang, Jianwei, 2022. "Will multi-industry supply chains' resilience under the impact of COVID-19 pandemic be different? A perspective from China's highway freight transport," Transport Policy, Elsevier, vol. 118(C), pages 165-178.
    12. Chen, Xi & Qiu, Yun & Shi, Wei & Yu, Pei, 2022. "Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic," China Economic Review, Elsevier, vol. 73(C).
    13. Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.
    14. Yanxin Wang & Jian Li & Xi Zhao & Gengzhong Feng & Xin (Robert) Luo, 2020. "Using Mobile Phone Data for Emergency Management: a Systematic Literature Review," Information Systems Frontiers, Springer, vol. 22(6), pages 1539-1559, December.
    15. Kai Zhang & Zixuan Chu & Jiping Xing & Honggang Zhang & Qixiu Cheng, 2023. "Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model," Mathematics, MDPI, vol. 11(19), pages 1-20, September.
    16. Yizhen Zhang & Zhen Deng & Agus Supriyadi & Rui Song & Tao Wang, 2022. "Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1694-1715, December.
    17. Mingke Xie & Yang Chen & Luliang Tang, 2022. "Exploring the Impact of Localized COVID-19 Events on Intercity Mobility during the Normalized Prevention and Control Period in China," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    18. Pan, Yu & He, Sylvia Y., 2022. "Analyzing COVID-19’s impact on the travel mobility of various social groups in China’s Greater Bay Area via mobile phone big data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 263-281.
    19. Tu, Yunbo & Meng, Xinzhu & Alzahrani, Abdullah Khames & Zhang, Tonghua, 2023. "Multi-objective optimization and nonlinear dynamics for sub-healthy COVID-19 epidemic model subject to self-diffusion and cross-diffusion," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    20. Jiansuo Pei & Gaaitzen de Vries & Meng Zhang, 2022. "International trade and Covid‐19: City‐level evidence from China's lockdown policy," Journal of Regional Science, Wiley Blackwell, vol. 62(3), pages 670-695, June.

    More about this item

    Keywords

    SARS-CoV-2; tourism; B.1.2; genomics;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:18:p:6748-:d:1238578. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.