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  1. Sep 2020
  2. academic.oup.com academic.oup.com
    Potential Biases Arising from Epidemic Dynamics in Observational Seroprotection Studies
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    1. ErikStuchly 08 Sep 2020

      Kahn, R., Kennedy-Shaffer, L., Grad, Y. H., Robins, J. M., & Lipsitch, M. (n.d.). Potential Biases Arising from Epidemic Dynamics in Observational Seroprotection Studies. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwaa188

      is:article lang:en COVID-19 dynamics bias seroprotection observational study duration immunity epidemiology confound modeling exposure geographic structure protection
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    academic.oup.com/aje/advance-article/doi/10.1093/aje/kwaa188/5900104
  3. cep.lse.ac.uk cep.lse.ac.uk
    Urban Density and COVID-19
    1
    1. ErikStuchly 07 Sep 2020

      Carozzi, F., Provenzano, S., Roth, S. (2020). Urban Density and Covid-19. Retrieved from http://cep.lse.ac.uk/pubs/download/dp1711.pdf

      is:article is:pdf lang:en COVID-19 urban density USA historical data geological feature mortality social media timing social distancing healthcare access mediation confound
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    cep.lse.ac.uk/pubs/download/dp1711.pdf
  4. epjdatascience.springeropen.com epjdatascience.springeropen.com
    Estimating community feedback effect on topic choice in social media with predictive modeling
    1
    1. ErikStuchly 03 Sep 2020

      Adelani, D. I., Kobayashi, R., Weber, I., & Grabowicz, P. A. (2020). Estimating community feedback effect on topic choice in social media with predictive modeling. EPJ Data Science, 9(1), 1–23. https://doi.org/10.1140/epjds/s13688-020-00243-w

      is:article lang:en social media community feedback estimation topic choice modeling prediction content confound susceptibility influence conformity
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    epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00243-w
  5. Aug 2020
  6. www.ijidonline.com www.ijidonline.com
    Effectiveness of Hydroxychloroquine in COVID-19 disease: A done and dusted situation?
    1
    1. ErikStuchly 21 Aug 2020

      Monforte, A. d’Arminio, Tavelli, A., Bai, F., Marchetti, G., & Cozzi-Lepri, A. (2020). Effectiveness of hydroxychloroquine in COVID-19 disease: A done and dusted deal? International Journal of Infectious Diseases, 99, 75–76. https://doi.org/10.1016/j.ijid.2020.07.056

      is:article lang:en COVID-19 hydroxychloroquine effectiveness intervention uncertainty clinical trial bias confound research criticism severity factor
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    ijidonline.com/article/S1201-9712(20)30600-7/fulltext
  7. covid-19.iza.org covid-19.iza.org
    COVID-19 and the Labor Market
    1
    1. ErikStuchly 04 Aug 2020

      Pandemic Meets Pollution: Poor Air Quality Increases Deaths by COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 31, 2020, from https://covid-19.iza.org/publications/dp13418/

      is:article lang:en COVID-19 mortality pollution air quality exposure transmission severity epidemiology confirmed cases particulate matter ozone regression confound concentration
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    covid-19.iza.org/publications/dp13418/
  8. covid-19.iza.org covid-19.iza.org
    COVID-19 and the Labor Market
    1
    1. ErikStuchly 04 Aug 2020

      Urban Density and COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 30, 2020, from https://covid-19.iza.org/publications/dp13440/

      is:article lang:en COVID-19 urban area density transmission severity USA confound historical population geological feature timing big data mortality morbidity Google Facebook social distancing age
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    covid-19.iza.org/publications/dp13440/
  9. Jul 2020
  10. twitter.com twitter.com
    Maarten van Smeden on Twitter: "This is a kind reminder that most issues with data (e.g. measurement error, incomplete data, confounding, selection) do *not* disappear just because you have N = ginormous" / Twitter
    1
    1. ErikStuchly 19 Jul 2020

      Maarten van Smeden on Twitter: “This is a kind reminder that most issues with data (e.g. measurement error, incomplete data, confounding, selection) do not disappear just because you have N = ginormous” / Twitter. (n.d.). Twitter. Retrieved July 19, 2020, from https://twitter.com/MaartenvSmeden/status/1283313496382373890

      is:tweet lang:en data collection measurement error incomplete data confound sample size bad science statistics research
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    twitter.com/MaartenvSmeden/status/1283313496382373890