@article{fdi:010078049,
  title = {{C}haos theory applied to the outbreak of {COVID}-19 : an ancillary approach to decision making in pandemic context},
  author = {{M}angiarotti, {S}ylvain and {P}eyre, {M}. and {Z}hang, {Y}. and {H}uc, {M}. and {R}oger, {F}. and {K}err, {Y}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}hile predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus is even more so. {T}he validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. {D}ata of the {COVID}-19 epidemics in {C}hina, {J}apan, {S}outh {K}orea and {I}taly were used to build up deterministic models without strong assumptions. {T}hese models were then applied to other countries to identify the closest scenarios in order to foresee their coming behaviour. {T}he models enabled to predict situations that were confirmed little by little, proving that these tools can be efficient and useful for decision making in a quickly evolving operational context.},
  keywords = {{C}oronavirus ; epidemics ; infectious disease control ; mathematical ; modelling ; pandemic ; {MONDE} ; {CHINE} ; {ITALIE} ; {COREE} {DU} {SUD} ; {JAPON} ; {EUROPE} ; {ETATS} {UNIS} ; {IRAN}},
  booktitle = {},
  journal = {{E}pidemiology and {I}nfection},
  volume = {148},
  numero = {},
  pages = {art. e95 [9p.]},
  ISSN = {0950-2688},
  year = {2020},
  DOI = {10.1017/s0950268820000990},
  URL = {https://www.documentation.ird.fr/hor/fdi:010078049},
}