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Response to COVID-19 with Probabilistic Programming

EasyChair Preprint no. 6949

2 pagesDate: October 30, 2021


This work provides an end-to-end pipeline to simulate the COVID-19 virus spread and the incurred loss of various non-pharmaceutical interventions.

Keyphrases: COVID-19 spread, economic impact, policy making, probabilistic programming, SEIRD model

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Assem Zhunis and Tung-Duong Mai and Sundong Kim},
  title = {Response to COVID-19 with Probabilistic Programming},
  howpublished = {EasyChair Preprint no. 6949},

  year = {EasyChair, 2021}}
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