Assessing bias in susceptible–infected–recovered estimation from aggregated epidemic data

The canonical susceptible–infected–recovered (SIR) epidemic model is ubiquitous in assessing severity to guide interventions. It is typically applied to hierarchically aggregated data from distinct sub-regions. The introduced heterogeneity can lead to significant errors in estimated epidemic severit...

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Autori principali:	Naijian Shen, Lydia Bourouiba
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	The Royal Society 2025-07-01
Serie:	Royal Society Open Science
Soggetti:	reproduction number estimation bias data aggregation spatial and temporal heterogeneity sub-epidemics
Accesso online:	https://royalsocietypublishing.org/doi/10.1098/rsos.240526
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