IDEAS home Printed from https://ideas.repec.org/a/spr/envsyd/v42y2022i3d10.1007_s10669-022-09865-z.html
   My bibliography  Save this article

Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States

Author

Listed:
  • Sheree A. Pagsuyoin

    (University of Massachusetts Lowell)

  • Gustavo Salcedo

    (University of Massachusetts Lowell)

  • Joost R. Santos

    (George Washington University)

  • Christopher B. Skinner

    (University of Massachusetts Lowell)

Abstract

In this paper, we analyzed the association among trends in COVID-19 cases, climate, air quality, and mobility changes during the first and second waves of the pandemic in five major metropolitan counties in the United States: Maricopa in Arizona, Cook in Illinois, Los Angeles in California, Suffolk in Massachusetts, and New York County in New York. These areas represent a range of climate conditions, geographies, economies, and state-mandated social distancing restrictions. In the first wave of the pandemic, cases were correlated with humidity in Maricopa, and temperature in Maricopa and Los Angeles. In Suffolk and New York, cases were correlated with mobility changes in recreation, grocery, parks, and transit stations. Neither cases nor death counts were strongly correlated with air quality. Periodic fluctuations in mobility were observed for residential areas during weekends, resulting in stronger correlation coefficients when only weekday datasets were included in the analysis. We also analyzed case-mobility correlations when mobility days were lagged, and found that the strongest correlation in the first wave occurred between 12 and 14 lag days (optimal at 13 days). There was stronger but greater variability in correlation coefficients across metropolitan areas in the first pandemic wave than in the second wave, notably in recreation areas and parks. In the second wave, there was less variability in correlations over lagged time and geographic locations. Overall, we did not find conclusive evidence to support associations between lower cases and climate in all areas. Furthermore, the differences in cases-mobility correlation trends during the two pandemic waves are indicative of the effects of travel restrictions in the early phase of the pandemic and gradual return to travel routines in the later phase. This study highlights the utility of mobility data in understanding the dynamics of disease transmission. It also emphasizes the criticality of timeline and local context in interpreting transmission trends. Mobility data can capture community response to local travel restrictions at different phases of their implementation and provide insights on how these responses evolve over time alongside disease trends.

Suggested Citation

  • Sheree A. Pagsuyoin & Gustavo Salcedo & Joost R. Santos & Christopher B. Skinner, 2022. "Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States," Environment Systems and Decisions, Springer, vol. 42(3), pages 350-361, September.
    • Handle: RePEc:spr:envsyd:v:42:y:2022:i:3:d:10.1007_s10669-022-09865-z
    DOI: 10.1007/s10669-022-09865-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10669-022-09865-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10669-022-09865-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benjamin F. Zaitchik & Neville Sweijd & Joy Shumake-Guillemot & Andy Morse & Chris Gordon & Aileen Marty & Juli Trtanj & Juerg Luterbacher & Joel Botai & Swadhin Behera & Yonglong Lu & Jane Olwoch & K, 2020. "A framework for research linking weather, climate and COVID-19," Nature Communications, Nature, vol. 11(1), pages 1-3, December.
    2. Yasuhiro Kubota & Takayuki Shiono & Buntarou Kusumoto & Junichi Fujinuma, 2020. "Multiple drivers of the COVID-19 spread: The roles of climate, international mobility, and region-specific conditions," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-15, September.
    3. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
    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. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    2. Saturnino Luz & Masood Masoodian, 2022. "Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    3. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    4. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    5. Askitas, Nikos & Tatsiramos, Konstantinos & Verheyden, Bertrand, 2020. "Lockdown Strategies, Mobility Patterns and COVID-19," IZA Discussion Papers 13293, Institute of Labor Economics (IZA).
    6. Jürgen Hackl & Thibaut Dubernet, 2019. "Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models," Future Internet, MDPI, vol. 11(4), pages 1-14, April.
    7. Hyeyoung Kim & Ningchuan Xiao & Mark Moritz & Rebecca Garabed & Laura W. Pomeroy, 2016. "Simulating the Transmission of Foot-And-Mouth Disease Among Mobile Herds in the Far North Region, Cameroon," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(2), pages 1-6.
    8. Yeran Sun & Hongchao Fan & Ming Li & Alexander Zipf, 2016. "Identifying the city center using human travel flows generated from location-based social networking data," Environment and Planning B, , vol. 43(3), pages 480-498, May.
    9. Kuo-Ying Wang, 2014. "How Change of Public Transportation Usage Reveals Fear of the SARS Virus in a City," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    10. Stipic, Dorian & Bradac, Mislav & Lipic, Tomislav & Podobnik, Boris, 2021. "Effects of quarantine disobedience and mobility restrictions on COVID-19 pandemic waves in dynamical networks," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    11. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    12. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    13. Tayo Alex Adekiya & Raphael Taiwo Aruleba & Babatunji Emmanuel Oyinloye & Kazeem Oare Okosun & Abidemi Paul Kappo, 2019. "The Effect of Climate Change and the Snail-Schistosome Cycle in Transmission and Bio-Control of Schistosomiasis in Sub-Saharan Africa," IJERPH, MDPI, vol. 17(1), pages 1-22, December.
    14. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    15. He, Pan & Lovo, Stefania & Veronesi, Marcella, 2022. "Social networks and renewable energy technology adoption: Empirical evidence from biogas adoption in China," Energy Economics, Elsevier, vol. 106(C).
    16. Jayanthi Rajarethinam & Janet Ong & Shi-Hui Lim & Yu-Heng Tay & Wacha Bounliphone & Chee-Seng Chong & Grace Yap & Lee-Ching Ng, 2019. "Using Human Movement Data to Identify Potential Areas of Zika Transmission: Case Study of the Largest Zika Cluster in Singapore," IJERPH, MDPI, vol. 16(5), pages 1-13, March.
    17. Michał Wielechowski & Katarzyna Czech & Łukasz Grzęda, 2020. "Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic," Economies, MDPI, vol. 8(4), pages 1-24, September.
    18. Burris, Courtney & Nikolaev, Alexander & Zhong, Shiran & Bian, Ling, 2021. "Network effects in influenza spread: The impact of mobility and socio-economic factors," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    19. Joji Abraham & Christopher Turville & Kim Dowling & Singarayer Florentine, 2021. "Does Climate Play Any Role in COVID-19 Spreading?—An Australian Perspective," IJERPH, MDPI, vol. 18(17), pages 1-14, August.
    20. Zhou Huang & Ganmin Yin & Xia Peng & Xiao Zhou & Quanhua Dong, 2023. "Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data," Environment and Planning B, , vol. 50(9), pages 2470-2490, November.

    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:spr:envsyd:v:42:y:2022:i:3:d:10.1007_s10669-022-09865-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.