Analytical simulation methodology for nonlinear spatiotemporal models: Spatial salience in Covid-19 contagion

Michael Beenstock, Yoel Cohen, Daniel Felsenstein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

‘Outdegree’ from directed graph theory is used to measure the salience of individual locations in the transmission of Covid-19 morbidity through the spatiotemporal network of contagion and their salience in the spatiotemporal diffusion of vaccination rollout. A spatial econometric model in which morbidity varies inversely with vaccination rollout, and vaccination rollout varies directly with morbidity is used to calculate dynamic auto-outdegrees for morbidity and dynamic cross-outdegrees for the effect of vaccination on morbidity. The former identifies hot spots of contagion, and the latter identifies locations in which vaccination rollout is particularly effective in reducing national morbidity. These outdegrees are calculated analytically rather than simulated numerically.

Original languageEnglish
Article number100844
JournalSpatial Statistics
Volume62
DOIs
StatePublished - Aug 2024

Bibliographical note

Publisher Copyright:
© 2024

Keywords

  • Covid-19 contagion
  • Graph theory
  • Network modeling
  • Spatial econometrics
  • Spatio-temporal diffusion

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