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Do Pandemics Obey the Elliott Wave Principle of Financial Markets?
Prashant Dogra; Eugene J. Koay; Zhihui Wang; Farhaan Vahidy; Mauro Ferrari; Renata Pasqualini; Wadih Arap; Marc L. Boom; H. Dirk Sostman; Vittorio Cristini.
Afiliação
  • Prashant Dogra; Houston Methodist Research Institute
  • Eugene J. Koay; MD Anderson Cancer Center
  • Zhihui Wang; Houston Methodist Research Institute
  • Farhaan Vahidy; Houston Methodist Research Institute
  • Mauro Ferrari; University of Washington
  • Renata Pasqualini; Rutgers Cancer Institute of New Jersey
  • Wadih Arap; Rutgers Cancer Institute of New Jersey
  • Marc L. Boom; Houston Methodist
  • H. Dirk Sostman; Houston Methodist Research Institute
  • Vittorio Cristini; Houston Methodist Research Institute
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21250273
ABSTRACT
The Elliott Wave principle is a time-honored, oft-used method for predicting variations in the financial markets. It is based on the notion that human emotions drive financial decisions. In the fight against COVID-19, human emotions are similarly decisive, for instance in that they determine ones willingness to be vaccinated, and/or to follow preventive measures including the wearing of masks, the application of social distancing protocols, and frequent handwashing. On this basis, we postulated that the Elliott Wave Principle may similarly be used to predict the future evolution of the COVID-19 pandemic. We demonstrated that this method reproduces the data pattern especially well for USA (daily new cases). Potential scenarios were then extrapolated, from the best-case corresponding to a rapid, full vaccination of the population, to the utterly disastrous case of slow vaccination, and poor adherence to preventive protocols.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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