IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2008.00298.html
   My bibliography  Save this paper

What can we learn about SARS-CoV-2 prevalence from testing and hospital data?

Author

Listed:
  • Daniel W. Sacks
  • Nir Menachemi
  • Peter Embi
  • Coady Wing

Abstract

Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and non-random segment of the population. However, people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population, yielding much tighter bounds on prevalence. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states.

Suggested Citation

  • Daniel W. Sacks & Nir Menachemi & Peter Embi & Coady Wing, 2020. "What can we learn about SARS-CoV-2 prevalence from testing and hospital data?," Papers 2008.00298, arXiv.org, revised Mar 2021.
    • Handle: RePEc:arx:papers:2008.00298
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2008.00298
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    2. Allcott, Hunt & Boxell, Levi & Conway, Jacob & Gentzkow, Matthew & Thaler, Michael & Yang, David, 2020. "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    3. Karl M. Aspelund & Michael C. Droste & James H. Stock & Christopher D. Walker, 2020. "Identification and Estimation of Undetected COVID-19 Cases Using Testing Data from Iceland," NBER Working Papers 27528, National Bureau of Economic Research, Inc.
    4. Tomas Philipson, 1996. "Private Vaccination and Public Health: An Empirical Examination for U.S. Measles," Journal of Human Resources, University of Wisconsin Press, vol. 31(3), pages 611-630.
    5. Philipson, Tomas, 2000. "Economic epidemiology and infectious diseases," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 33, pages 1761-1799, Elsevier.
    6. Michael Kremer, 1996. "Integrating Behavioral Choice into Epidemiological Models of AIDS," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 549-573.
    7. Michael Kremer, 1996. "Integrating Behavioral Choice into Epidemiological Models of the AIDS Epidemic," NBER Working Papers 5428, National Bureau of Economic Research, Inc.
    8. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    9. Sumedha Gupta & Thuy D. Nguyen & Felipe Lozano Rojas & Shyam Raman & Byungkyu Lee & Ana Bento & Kosali I. Simon & Coady Wing, 2020. "Tracking Public and Private Responses to the COVID-19 Epidemic: Evidence from State and Local Government Actions," NBER Working Papers 27027, 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. Bollinger, Christopher R. & van Hasselt, Martijn, 2020. "Estimating the cumulative rate of SARS-CoV-2 infection," Economics Letters, Elsevier, vol. 197(C).

    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. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    2. Auld, M. Christopher, 2003. "Choices, beliefs, and infectious disease dynamics," Journal of Health Economics, Elsevier, vol. 22(3), pages 361-377, May.
    3. Rikard Forslid & Mathias Herzing, 2015. "On the Optimal Production Capacity for Influenza Vaccine," Health Economics, John Wiley & Sons, Ltd., vol. 24(6), pages 726-741, June.
    4. Oster, Emily, 2018. "Does disease cause vaccination? Disease outbreaks and vaccination response," Journal of Health Economics, Elsevier, vol. 57(C), pages 90-101.
    5. Skatun, John Douglas, 2003. "The overprovision of infectious disease medicine," Economics Letters, Elsevier, vol. 80(1), pages 61-66, July.
    6. Cronin, Christopher J. & Evans, William N., 2021. "Total shutdowns, targeted restrictions, or individual responsibility: How to promote social distancing in the COVID-19 Era?," Journal of Health Economics, Elsevier, vol. 79(C).
    7. Goodkin-Gold, Matthew & Kremer, Michael & Snyder, Christopher M. & Williams, Heidi, 2022. "Optimal vaccine subsidies for endemic diseases," International Journal of Industrial Organization, Elsevier, vol. 84(C).
    8. Áureo De Paula & Gil Shapira & Petra E. Todd, 2014. "How Beliefs About Hiv Status Affect Risky Behaviors: Evidence From Malawi," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(6), pages 944-964, September.
    9. Auld, M. Christopher & Toxvaerd, Flavio, 2021. "The Great Covid-19 Vaccine Rollout: Behavioural And Policy Responses," National Institute Economic Review, National Institute of Economic and Social Research, vol. 257, pages 14-35, August.
    10. Miguel Casares & Paul Gomme & Hashmat Khan, 2022. "COVID‐19 pandemic and economic scenarios for Ontario," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 503-539, February.
    11. Farboodi, Maryam & Jarosch, Gregor & Shimer, Robert, 2021. "Internal and external effects of social distancing in a pandemic," Journal of Economic Theory, Elsevier, vol. 196(C).
    12. Casey B. Mulligan, 2021. "The Backward Art of Slowing the Spread? Congregation Efficiencies during COVID-19," NBER Working Papers 28737, National Bureau of Economic Research, Inc.
    13. Jérôme Adda, 2016. "Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 891-941.
    14. Jung, Juergen & Manley, James & Shrestha, Vinish, 2021. "Coronavirus infections and deaths by poverty status: The effects of social distancing," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 311-330.
    15. David E. Bloom & Michael Kuhn & Klaus Prettner, 2022. "Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses," Journal of Economic Literature, American Economic Association, vol. 60(1), pages 85-131, March.
    16. Adeline Delavande & Dana Goldman & Neeraj Sood, 2010. "Criminal Prosecution and Human Immunodeficiency Virus-Related Risky Behavior," Journal of Law and Economics, University of Chicago Press, vol. 53(4), pages 741-782.
    17. Gabriel Picone & Robyn Kibler & Benedicte Apouey, 2013. "Individuals� Preventive Behavioral Response to Changes in Malaria Risks and Government Interventions: Evidence from six African countries," Working Papers 0313, University of South Florida, Department of Economics.
    18. Goenka, Aditya & Liu, Lin & Nguyen, Manh-Hung, 2014. "Infectious diseases and economic growth," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 34-53.
    19. Joshua S. Gans, 2020. "The Economic Consequences of R̂ = 1: Towards a Workable Behavioural Epidemiological Model of Pandemics," NBER Working Papers 27632, National Bureau of Economic Research, Inc.
    20. Regina Pleninger & Sina Streicher & Jan-Egbert Sturm, 2022. "Do COVID-19 containment measures work? Evidence from Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-24, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2008.00298. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.