NetworkSIR and EnvironmentalSIR: Effective, Open-Source Epidemic Modeling in the Absence of Data

Madison A. Pickering, Subbarayan Venkatesan, Christoph Lehmann, Sameh N Saleh, Richard J. Medford

Research output: Contribution to journalArticlepeer-review

Abstract

The rapidly changing situation characterized by the COVID-19 pandemic highlighted a need for new epidemic modeling strategies. Due to an absence of computationally efficient models robust to paucity of reliable data, we developed NetworkSIR, a model capable of making predictions when only the approximate population density is known. We then extend NetworkSIR to capture the effect of indirect disease spread on the progression of an epidemic (EnvironmentalSIR).

Original languageEnglish (US)
Pages (from-to)1009-1018
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of 'NetworkSIR and EnvironmentalSIR: Effective, Open-Source Epidemic Modeling in the Absence of Data'. Together they form a unique fingerprint.

Cite this