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@ARTICLE{Paul:462885,
      author       = {Paul, Ayan and Bhattacharjee, Jayanta Kumar and Pal, Akshay
                      and Chakraborty, Sagar},
      title        = {{E}mergence of universality in the transmission dynamics of
                      {COVID}-19},
      journal      = {Scientific reports},
      volume       = {11},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {PUBDB-2021-03580, DESY-20-234. HU-EP-20/42.
                      arXiv:2101.12556},
      pages        = {18891},
      year         = {2021},
      note         = {14 pages and 6 figures (updated figure captions and
                      reference list)},
      abstract     = {The complexities involved in modeling the transmission
                      dynamics of COVID-19 has been a major roadblock in achieving
                      predictability in the spread and containment of the disease.
                      In addition to understanding the modes of transmission, the
                      effectiveness of the mitigation methods also needs to be
                      built into any effective model for making such predictions.
                      We show that such complexities can be circumvented by
                      appealing to scaling principles which lead to the emergence
                      of universality in the transmission dynamics of the disease.
                      The ensuing data collapse renders the transmission dynamics
                      largely independent of geopolitical variations, the
                      effectiveness of various mitigation strategies, population
                      demographics, etc. We propose a simple two-parameter model
                      -- the Blue Sky model -- and show that one class of
                      transmission dynamics can be explained by a solution that
                      lives at the edge of a blue sky bifurcation. In addition,
                      the data collapse leads to an enhanced degree of
                      predictability in the disease spread for several
                      geographical scales which can also be realized in a
                      model-independent manner as we show using a deep neural
                      network. The methodology adopted in this work can
                      potentially be applied to the transmission of other
                      infectious diseases and new universality classes may be
                      found. The predictability in transmission dynamics and the
                      simplicity of our methodology can help in building policies
                      for exit strategies and mitigation methods during a
                      pandemic.},
      cin          = {T},
      ddc          = {600},
      cid          = {I:(DE-H253)T-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2101.12556},
      howpublished = {arXiv:2101.12556},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2101.12556;\%\%$},
      pubmed       = {34556753},
      UT           = {WOS:000698791600113},
      doi          = {10.1038/s41598-021-98302-3},
      url          = {https://bib-pubdb1.desy.de/record/462885},
}