Using Quantifiable Behavioral Traits to Predict a Country's COVID19 Infection Rates

COVID19 has shown that indicators that are a function of a nation's economy and healthcare infrastructure are inaccurate in predicting a country's outcomes should a health pandemic occur. Our poster suggests the utilization of quantifiable traits like Individualism, Power Distance, Masculinity, Uncertainty avoidance, long-term orientation, and Indulgence to predict a country's COVID19 infection rates. This is accomplished by applying machine learning techniques like CART imputation and Poisson regression against COVID and behavioral datasets.

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  • poster.pdf

    size: 738 KB | mime_type: application/pdf | date: 2021-07-16

Metadata

Work Title Using Quantifiable Behavioral Traits to Predict a Country's COVID19 Infection Rates
Access
Open Access
Creators
  1. Charles Alba
  2. Manasvi Mittal
  3. Anmolika Singh
Keyword
  1. COVID19
  2. Health Policy
  3. Socio-cultural Behaviors
  4. Socio-psychology
License CC BY 4.0 (Attribution)
Work Type Poster
Publisher
  1. 2021 Undergraduate Exhbition (Social and Behavioral Sciences)
Publication Date April 2021
Subject
  1. Socio-psychology
  2. Health Policy
Language
  1. English
Publisher Identifier (DOI)
  1. 10.13140/RG.2.2.20276.58240
Related URLs
Deposited July 16, 2021

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Version 1
published

  • Created
  • Added Creator Charles Alba
  • Added Creator Manasvi Mittal
  • Added Creator Anmolika Singh
  • Added poster.pdf
  • Updated License Show Changes
    License
    • https://creativecommons.org/licenses/by/4.0/
  • Published
  • Updated

Version 2
published

  • Created
  • Deleted poster.pdf
  • Added poster.pdf
  • Published
  • Updated