Sunday, 22 March 2020

Computational Epidemiology and Data Scientists: Don't post analysis on outbreak arbitrarily

Summary

Many data scientist are trained or experienced in using tools to do statistical modelling, forecasting or machine learning solutions, this doesn't necessarily mean that they should just jump out and do an ad-hoc analysis on the available public data on the covid19 outbreak and draw policy conclusions and publish them in their blogs or the other medium.  Rule of thumb of doing such thing you should have at least one published paper, article or software solution related to outbreaks appeared before December 2019. Please be considerate, epidemiological modelling is not merely fitting exponential distribution.

Conclusion
Please refrain on posting a blog or similar posts on infection modelling and giving advice out of your ad-hoc data analysis you did over your lunch-break, if you have not worked on computational epidemiology before. There is a vast academic literature on computational epidemiology. Let people experts in those fields express their modelling efforts first. Let us value expertise in an area.

Appendix: Computational epidemiology introductory resources

Here we provide, limited pointers to computational epidemiology literature. Google Scholar is your friend to find many more resources.

  • Computational Epidemiology
    Madhav Marathe, Anil Kumar S. Vullikanti
    Communications of the ACM, July 2013, Vol. 56 No. 7, Pages 88-96 doi
  • Broadwick: a framework for computational epidemiology
    O’Hare, A., Lycett, S.J., Doherty, T. et al. Broadwick
    BMC Bioinformatics 17, 65 (2016).
    doi
  • Mathematical Tools for Understanding Infectious Disease Dynamics
    (Princeton Series in Theoretical and Computational Biology)
    Odo Diekmann, Hans Heesterbeek, and Tom Britton
    Princeton Press
  • Agent-Based Simulation Tools in Computational Epidemiology
    Patlolla P., Gunupudi V., Mikler A.R., Jacob R.T. (2006)
    doi
  • DIMACS 2002-2011 Special Focus on Computational and Mathematical Epidemiology Rutgers working group
  • Containment strategy for an epidemic based on fluctuations in the SIR model
    Philip Bittihn, Ramin Golestanian
    Oxford/Max Planck
    arXiv:2003.08784
  • SIAM Epidemiology Collection (2020)
  • The collection provided by the American Physical Society (APS) Physical Review COVID-19 collection
  • Modeling epidemics by the lattice Boltzmann method Alessandro De Rosis Phys. Rev. E 102, 023301



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