Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Modeling Epidemics: A Primer and Numerus Software Implementation

View ORCID ProfileWayne M. Getz, Richard Salter, Oliver Muellerklein, Hyun S. Yoon, Krti Tallam
doi: https://doi.org/10.1101/191601
Wayne M. Getz
1Dept. ESPM, UC Berkeley, CA 94720-3114, USA
2School of Mathematical Sciences, University of KwaZulu-Natal Private Bag X54001, Durban 4000, South Africa
3Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Wayne M. Getz
Richard Salter
3Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA
4Computer Science Dept., Oberlin College, Oberlin, Ohio, OH 44074, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Oliver Muellerklein
1Dept. ESPM, UC Berkeley, CA 94720-3114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hyun S. Yoon
1Dept. ESPM, UC Berkeley, CA 94720-3114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Krti Tallam
1Dept. ESPM, UC Berkeley, CA 94720-3114, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Epidemiological models are dominated by SEIR (Susceptible, Exposed, Infected and Removed) dynamical systems formulations and their elaborations. These formulations can be continuous or discrete, deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SEIR dynamical systems models, and we outline how they can be easily and rapidly constructed using the Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using both the Numerus Model Builder network and geographical mapping tools.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted December 13, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Modeling Epidemics: A Primer and Numerus Software Implementation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Modeling Epidemics: A Primer and Numerus Software Implementation
Wayne M. Getz, Richard Salter, Oliver Muellerklein, Hyun S. Yoon, Krti Tallam
bioRxiv 191601; doi: https://doi.org/10.1101/191601
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Modeling Epidemics: A Primer and Numerus Software Implementation
Wayne M. Getz, Richard Salter, Oliver Muellerklein, Hyun S. Yoon, Krti Tallam
bioRxiv 191601; doi: https://doi.org/10.1101/191601

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Epidemiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4231)
  • Biochemistry (9123)
  • Bioengineering (6769)
  • Bioinformatics (23971)
  • Biophysics (12110)
  • Cancer Biology (9511)
  • Cell Biology (13754)
  • Clinical Trials (138)
  • Developmental Biology (7623)
  • Ecology (11678)
  • Epidemiology (2066)
  • Evolutionary Biology (15495)
  • Genetics (10633)
  • Genomics (14312)
  • Immunology (9474)
  • Microbiology (22825)
  • Molecular Biology (9087)
  • Neuroscience (48922)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2566)
  • Physiology (3842)
  • Plant Biology (8322)
  • Scientific Communication and Education (1468)
  • Synthetic Biology (2295)
  • Systems Biology (6183)
  • Zoology (1299)