Is the worst of the COVID-19 global pandemic yet to come? Application of financial mathematics as candidate predictive tools.


Journal

Translational psychiatry
ISSN: 2158-3188
Titre abrégé: Transl Psychiatry
Pays: United States
ID NLM: 101562664

Informations de publication

Date de publication:
20 05 2021
Historique:
received: 17 02 2021
accepted: 04 05 2021
revised: 21 04 2021
entrez: 21 5 2021
pubmed: 22 5 2021
medline: 22 6 2021
Statut: epublish

Résumé

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 the COVID-19 global pandemic, human emotions are similarly decisive, for instance in that they determine one's willingness to be vaccinated, and/or to follow preventive measures including the personal 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 for various countries and the world (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.

Identifiants

pubmed: 34016952
doi: 10.1038/s41398-021-01429-0
pii: 10.1038/s41398-021-01429-0
pmc: PMC8134815
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

299

Subventions

Organisme : National Science Foundation (NSF)
ID : DMS-1930583
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U54CA210181
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : 1R01CA222007
Organisme : U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER)
ID : 1R01CA226537
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : 1U01CA213759
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : 1R01CA226537
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : 1U01CA196403

Commentaires et corrections

Type : UpdateOf

Références

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Auteurs

Prashant Dogra (P)

Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.

Eugene J Koay (EJ)

Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Zhihui Wang (Z)

Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.

Farhaan S Vahidy (FS)

Center for Outcomes Research, Houston Methodist Research Institute, Houston, TX, USA.
Houston Methodist Neurological Institute, Houston Methodist, Houston, TX, USA.

Mauro Ferrari (M)

Department of Pharmaceutics, University of Washington, Seattle, WA, USA.
Dompé X-Therapeutics, San Mateo, CA, USA.

Renata Pasqualini (R)

Rutgers Cancer Institute of New Jersey, Newark, NJ, USA.
Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA.

Wadih Arap (W)

Rutgers Cancer Institute of New Jersey, Newark, NJ, USA.
Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA.

Marc L Boom (ML)

Department of Medicine, Houston Methodist, Houston, TX, USA.

H Dirk Sostman (H)

Weill Cornell Medicine, New York, NY, USA.
Houston Methodist Research Institute, Houston, TX, USA.
Houston Methodist Academic Institute, Houston, TX, USA.

Vittorio Cristini (V)

Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. vcristini@houstonmethodist.org.

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Classifications MeSH