Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/28132.html
   My bibliography  Save this paper

Geospatial Analysis of the September 2020 Coronavirus Outbreak at the University of Wisconsin – Madison: Did a Cluster of Local Bars Play a Critical Role?

Author

Listed:
  • Jeffrey E. Harris
Abstract
We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus outbreak at the University of Wisconsin-Madison campus, where nearly three thousand students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicenter of the outbreak, in close proximity to on-campus residence halls and off-campus housing. Smartphones originating from the two hardest hit residence halls (Sellery and Witte), where about one in five students were infected, were 2.95 times more likely to visit the 20-bar cluster than smartphones originating in two more distant, less affected residence halls (Ogg and Smith). By contrast, smartphones from Sellery-Witte were only 1.55 times more likely than those from Ogg-Smith to visit a group of 68 restaurants in the same area. Physical proximity thus had a much stronger influence on bar visitation than on restaurant visitation (rate ratio 1.91, 95% CI 1.29-2.85, p = 0.0007). In a separate analysis, we determined the per-capita rates of visitation to the 20-bar cluster and to the 68-restaurant comparison group by smartphones originating in each of 19 census tracts in the university area, and related these visitation rates to the per-capita incidence of newly positive coronavirus tests in each census tract. In a multivariate regression, the visitation rate to the bar cluster was a significant determinant of infection rates (elasticity 0.90, 95% CI 0.26-1.54, p = 0.009), while the restaurant visitation rate showed no such relationship. Researchers and public health professionals need to think more about the potential super-spreader effects of clusters and networks of places, rather than individual sites.

Suggested Citation

  • Jeffrey E. Harris, 2020. "Geospatial Analysis of the September 2020 Coronavirus Outbreak at the University of Wisconsin – Madison: Did a Cluster of Local Bars Play a Critical Role?," NBER Working Papers 28132, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28132
    Note: EH PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w28132.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward L. Glaeser & Caitlin S. Gorback & Stephen J. Redding, 2020. "How Much Does COVID-19 Increase with Mobility? Evidence from New York and Four Other U.S. Cities," Working Papers 2020-22, Princeton University. Economics Department..
    2. Jeffrey E. Harris, 2020. "The Coronavirus Epidemic Curve is Already Flattening in New York City," NBER Working Papers 26917, National Bureau of Economic Research, Inc.
    3. Rothman, K.J., 1982. "Spermicide use and Down's syndrome," American Journal of Public Health, American Public Health Association, vol. 72(4), pages 399-401.
    4. Dhaval M. Dave & Andrew I. Friedson & Drew McNichols & Joseph J. Sabia, 2020. "The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19," NBER Working Papers 27813, National Bureau of Economic Research, Inc.
    5. Jeffrey E. Harris, 2020. "Correction to: Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults," Review of Economics of the Household, Springer, vol. 18(4), pages 1039-1039, December.
    6. Jeffrey E. Harris, 2020. "Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults," Review of Economics of the Household, Springer, vol. 18(4), pages 1019-1037, December.
    7. Luis Orea & Inmaculada C. Álvarez, 2020. "How effective has been the Spanish lockdown to battle COVID-19? A spatial analysis of the coronavirus propagation across provinces," Working Papers 2020-03, FEDEA.
    8. Jeffrey E. Harris, 2020. "The Subways Seeded the Massive Coronavirus Epidemic in New York City," NBER Working Papers 27021, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jeffrey E. Harris, 2021. "Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission," Journal of Bioeconomics, Springer, vol. 23(1), pages 55-83, April.
    2. Giménez, Víctor & Prior, Diego & Thieme, Claudio & Tortosa-Ausina, Emili, 2024. "International comparisons of COVID-19 pandemic management: What can be learned from activity analysis techniques?," Omega, Elsevier, vol. 122(C).
    3. Martin Lange & Ole Monscheuer, 2022. "Spreading the disease: Protest in times of pandemics," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2664-2679, December.
    4. Krzysztof Zaremba, 2023. "Opening of hotels and ski facilities: Impact on mobility, spending, and Covid‐19 outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1148-1180, May.

    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. Jeffrey E. Harris, 2021. "Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission," Journal of Bioeconomics, Springer, vol. 23(1), pages 55-83, April.
    2. de Palma, André & Vosough, Shaghayegh & Liao, Feixiong, 2022. "An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 372-397.
    3. Aparicio Fenoll, Ainoa & Grossbard, Shoshana, 2020. "Intergenerational residence patterns and Covid-19 fatalities in the EU and the US," Economics & Human Biology, Elsevier, vol. 39(C).
    4. Chad Cotti & Bryan Engelhardt & Joshua Foster & Erik Nesson & Paul Niekamp, 2021. "The relationship between in‐person voting and COVID‐19: Evidence from the Wisconsin primary," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 760-777, October.
    5. Borsati, Mattia & Nocera, Silvio & Percoco, Marco, 2022. "Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy," Research in Transportation Economics, Elsevier, vol. 95(C).
    6. INOUE Tomoo & OKIMOTO Tatsuyoshi, 2022. "Exploring the Dynamic Relationship between Mobility and the Spread of COVID-19, and the Role of Vaccines," Discussion papers 22011, Research Institute of Economy, Trade and Industry (RIETI).
    7. Jeffrey E. Harris, 2020. "Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults," Review of Economics of the Household, Springer, vol. 18(4), pages 1019-1037, December.
    8. Ján Palguta & Levínský, René & Škoda, Samuel, 2021. "Do Elections Accelerate the COVID-19 Pandemic? Evidence from a Natural Experiment," GLO Discussion Paper Series 891, Global Labor Organization (GLO).
    9. Pensieroso, Luca & Sommacal, Alessandro & Spolverini, Gaia, 2023. "Intergenerational coresidence and the Covid-19 pandemic in the United States," Economics & Human Biology, Elsevier, vol. 49(C).
    10. Cristini, Annalisa & Trivin, Pedro, 2022. "Close encounters during a pandemic: Social habits and inter-generational links in the first two waves of COVID-19," Economics & Human Biology, Elsevier, vol. 47(C).
    11. Rui Li & Youqin Huang, 2023. "COVID-19 pandemic and minority health disparities in New York City: A spatial and temporal perspective," Environment and Planning B, , vol. 50(5), pages 1194-1211, June.
    12. Daeyoung Kwon & Sung Eun Sally Oh & Sangwon Choi & Brian H. S. Kim, 2023. "Viability of compact cities in the post-COVID-19 era: subway ridership variations in Seoul Korea," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(1), pages 175-203, August.
    13. Harris, Jeffrey E., 2020. "COVID-19, bar crowding, and the Wisconsin Supreme Court: A non-linear tale of two counties," Research in International Business and Finance, Elsevier, vol. 54(C).
    14. Mangrum, Daniel & Niekamp, Paul, 2022. "JUE Insight: College student travel contributed to local COVID-19 spread," Journal of Urban Economics, Elsevier, vol. 127(C).
    15. Jeffrey E. Harris, 2020. "Reopening Under COVID-19: What to Watch For," NBER Working Papers 27166, National Bureau of Economic Research, Inc.
    16. George Davis, 2021. "The many ways COVID-19 affects households: consumption, time, and health outcomes," Review of Economics of the Household, Springer, vol. 19(2), pages 281-289, June.
    17. Rongxiang Rui & Maozai Tian & Man-Lai Tang & George To-Sum Ho & Chun-Ho Wu, 2021. "Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    18. Egor Malkov, 2021. "Spousal Occupational Sorting and COVID-19 Incidence: Evidence from the United States," Papers 2107.14350, arXiv.org, revised Sep 2021.
    19. Milena Almagro & Joshua Coven & Arpit Gupta & Angelo Orane-Hutchinson, 2020. "Racial Disparities in Frontline Workers and Housing Crowding during COVID-19: Evidence from Geolocation Data," Opportunity and Inclusive Growth Institute Working Papers 37, Federal Reserve Bank of Minneapolis.
    20. Jose Olmo & Marcos Sanso‐Navarro, 2021. "Modeling the spread of COVID‐19 in New York City," Papers in Regional Science, Wiley Blackwell, vol. 100(5), pages 1209-1229, October.

    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

    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:nbr:nberwo:28132. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.