Work

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

Year: 2021

Type: article

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 one's 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.

Source: Translational Psychiatry

DOI: 10.1038/s41398-021-01429-0

Authors: Prashant Dogra, Eugene J. Koay, Zhihui Wang, Farhaan S. Vahidy, Mauro Ferrari, Renata Pasqualini, Wadih Arap, Marc L. Boom, H. Dirk Sostman, Vittorio Cristini

Institutions: Houston Methodist, Houston Methodist Research Institute, The University of Texas MD Anderson Cancer Center, University of Washington, Rutgers Cancer Institute, Rutgers New Jersey Medical School, Weill Cornell Medicine

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,380

Funders: National Science Foundation, U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Office of Extramural Research, National Institutes of Health

Awards: Center for Immunotherapeutic Transport Oncophysics, Targeted therapeutics for ovarian cancer and its microenvironment - treatment and theoretical modeling, G2324600623, G2742187648, G3375337788, G4947547527, G5360353628, G5637555674, G6217058740, Collaborative Research: A New Multiscale Methodology and Application to Tumor Growth Modeling, G6675610623, Imaging and Molecular Correlates of Progression in Cystic Neoplasms of the Pancreas, G710591248, A Targeted Nanomedicine Prototype Against Enzalutamide-resistant Prostate Cancer, G7600357429, miR-155 targeted therapeutics for precision medicine in lung cancer, G8654816747

OpenAlex ID: W3163603767

FWCI: 0.872

Cites: 14

Cited by: 12

Related to: 10