6 Matching Annotations
  1. Oct 2020
  2. arxiv.org arxiv.org
    Disruption in the Chinese E-Commerce During COVID-19:A Case Study of the Beidian Platform
    1
    1. katietaylor_99 02 Oct 2020

      Yuan, Yuan, Muzhi Guan, Zhilun Zhou, Sundong Kim, Meeyoung Cha, and Yong Li. ‘Disruption in the Chinese E-Commerce During COVID-19:A Case Study of the Beidian Platform’. ArXiv:2009.14605 [Physics], 22 July 2020. http://arxiv.org/abs/2009.14605.

      is:report lang:en COVID-19 Chinese E-Commerce Case study disruption online shopping time series analysis essential goods
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    arxiv.org/abs/2009.14605
  3. Aug 2020
  4. osf.io osf.io
    Could Ramadan catalyze SARS-CoV-2 spread? Preliminary results
    1
    1. ErikStuchly 28 Aug 2020

      Jarynowski, A., & Płatek, D. (2020). Could Ramadan catalyze or inhibit SARS-CoV-2 spread? Preliminary results [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/725dv

      is:preprint lang:en COVID-19 Ramadan transmission Muslim celebration attendance worship site extended family contact rate fasting mitigation measure impact short-term effect analysis time series case increase positive link epidemiology
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    osf.io/preprints/socarxiv/725dv/
  5. www.medrxiv.org www.medrxiv.org
    Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers
    1
    1. ErikStuchly 28 Aug 2020

      Starnini, M., Aleta, A., Tizzoni, M., & Moreno, Y. (2020). Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers. MedRxiv, 2020.06.26.20140871. https://doi.org/10.1101/2020.06.26.20140871

      is:article lang:en COVID-19 data analysis R Time-varying reproduction number Italy Spain symptom dynamics real time-series onset
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    medrxiv.org/content/10.1101/2020.06.26.20140871v1
  6. May 2020
  7. www.medrxiv.org www.medrxiv.org
    https://doi.org/10.1101/2020.04.15.20067074
    1
    1. Marlene_Wulf 05 May 2020

      Modi, C., Boehm, V., Ferraro, S., Stein, G., & Seljak, U. (2020). Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis. MedRxiv, 2020.04.15.20067074. https://doi.org/10.1101/2020.04.15.20067074

      is:article lang:en COVID-19 mortality Italy age dependance analysis time-series data science method
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    medrxiv.org/cgi/content/10.1101/2020.04.15.20067074
  8. psyarxiv.com psyarxiv.com
    Complexity In Psychological Self-Ratings: Implications for research and practice
    1
    1. edampf 04 May 2020

      Olthof, M., Hasselman, F., & Lichtwarck-Aschoff, A. (2020, May 1). Complexity In Psychological Self-Ratings: Implications for research and practice. Retrieved from psyarxiv.com/fbta8

      is:preprint lang:en psychology complexity mental health ecological momentary assessment experience sampling psychopathology time series personalized medicine survey self-report complex system research data analysis
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    psyarxiv.com/fbta8/
  9. Nov 2019
  10. a-little-book-of-r-for-time-series.readthedocs.io a-little-book-of-r-for-time-series.readthedocs.io
    Using R for Time Series Analysis — Time Series 0.2 documentation
    1
    1. udaybhaskar 07 Nov 2019
      This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

      what is time series?

      time series Statistics data analysis
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    a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html