Analytical Modelling of the Spread of Disease in Confined and Crowded Spaces

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
223 Downloads (Pure)

Abstract

Since 1927 and until recently, most models describing the spread of disease have been of compartmental type, based on the assumption that populations are homogeneous and well-mixed. Recent models have utilised agent-based models and complex networks to explicitly study heterogeneous interaction patterns, but this leads to an increasing computational complexity. Compartmental models are appealing because of their simplicity, but their parameters, especially the transmission rate, are complex and depend on a number of factors, which makes it hard to predict how a change of a single environmental, demographic, or epidemiological factor will affect the population. Therefore, in this contribution we propose a middle ground, utilising crowd-behaviour research to improve compartmental models in crowded situations. We show how both the rate of infection as well as the walking speed depend on the local crowd density around an infected individual. The combined effect is that the rate of infection at a population scale has an analytically tractable non-linear dependency on crowd density. We model the spread of a hypothetical disease in a corridor and compare our new model with a typical compartmental model, which highlights the regime in which current models may not produce credible results.
Original languageEnglish
Article number4856
Number of pages6
JournalScientific Reports
Volume4
DOIs
Publication statusPublished - 6 May 2014

Keywords

  • Infectious diseases
  • Statistical physics

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