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Pranav Pandit
PeerJ Author & Reviewer
340 Points

Contributions by role

Author 235
Reviewer 105

Contributions by subject area

Microbiology
Veterinary Medicine
Epidemiology
Data Mining and Machine Learning
Conservation Biology
Ecology
Zoology
Mathematical Biology
Infectious Diseases
Bioinformatics
Global Health
Statistics
Data Science

Pranav S Pandit

PeerJ Author & Reviewer

Summary

I am a veterinary epidemiologist with a research interest in the ecology of diseases in animal populations and studying emerging infectious diseases that spillover from animals to humans. My expertise lies in using mathematical and machine learning models to unravel drivers affecting disease epidemiology and transmission in animal populations and its spillover to humans.

Animal Behavior Conservation Biology Data Mining & Machine Learning Epidemiology Global Health Infectious Diseases Mathematical Biology Virology

Past or current institution affiliations

UC Davis

Identities

@PanditPranav

Websites

  • Google Scholar

PeerJ Contributions

  • Articles 2
  • Reviewed 2
July 16, 2021
Dairy management practices associated with multi-drug resistant fecal commensals and Salmonella in cull cows: a machine learning approach
Pranav S. Pandit, Deniece R. Williams, Paul Rossitto, John M. Adaska, Richard Pereira, Terry W. Lehenbauer, Barbara A. Byrne, Xunde Li, Edward R. Atwill, Sharif S. Aly
https://doi.org/10.7717/peerj.11732 PubMed 34316397
April 13, 2021
Retrospective study on admission trends of Californian hummingbirds found in urban habitats (1991–2016)
Pranav S. Pandit, Ruta R. Bandivadekar, Christine K. Johnson, Nicole Mikoni, Michelle Mah, Guthrum Purdin, Elaine Ibarra, Duane Tom, Allison Daugherty, Max W. Lipman, Krystal Woo, Lisa A. Tell
https://doi.org/10.7717/peerj.11131 PubMed 33954034

Signed reviews submitted for articles published in PeerJ Note that some articles may not have the review itself made public unless authors have made them open as well.

January 11, 2021
SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials
Xiaobo Zhong, Bin Cheng, Xinru Wang, Ying Kuen Cheung
https://doi.org/10.7717/peerj.10559 PubMed 33510969
January 7, 2021
Reproducing country-wide COVID-19 dynamics can require the usage of a set of SIR systems
Eugene B. Postnikov
https://doi.org/10.7717/peerj.10679 PubMed 33505808