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 language | English |
|---|---|
| Article number | 100844 |
| Journal | Spatial Statistics |
| Volume | 62 |
| DOIs | |
| State | Published - Aug 2024 |
Bibliographical note
Publisher Copyright:© 2024
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Covid-19 contagion
- Graph theory
- Network modeling
- Spatial econometrics
- Spatio-temporal diffusion
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