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Behzad Vahedi

Biographic Data

ID7931760
NAMEBehzad Vahedi
GIVEN NAMESBehzad
FAMILY NAMEVahedi
SIGNATUREVAHEDI B
AFFILIATIONSUniversity of California, Santa Barbara
ORCID0000-0001-5782-3831
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2020
H-INDEX0
  • GIS-based spatial modeling of Covid-19 incidence rate in the continental United States

    Open Access•Abolfazl Mollalo, Behzad Vahedi et al.•ARTICLE•The Science of The Total…•2020

  • Artificial Neural Network Modeling of Novel Coronavirus (Covid-19) Incidence Rates across the Continental United States

    Open Access•Abolfazl Mollalo, Kiara M Rivera et al.•ARTICLE•International Journal of…•2020

    Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables…

No prominent works on this page.

  • GIS-based spatial modeling of Covid-19 incidence rate in the continental United States

    Open Access•Abolfazl Mollalo, Behzad Vahedi et al.•ARTICLE•The Science of The Total…•2020

  • Artificial Neural Network Modeling of Novel Coronavirus (Covid-19) Incidence Rates across the Continental United States

    Open Access•Abolfazl Mollalo, Kiara M Rivera et al.•ARTICLE•International Journal of…•2020

    Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables…

COVID-19 epidemiological studies (2 works) · Data-Driven Disease Surveillance (2 works) · Demography (2 works) · Environmental health (2 works) · Geography (2 works) · Mathematics (2 works) · Medicine (2 works) · Population (2 works) · Socioeconomic status (2 works) · Coronavirus disease 2019 (COVID-19) (1 works)

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