Analytical modelling of the spread of disease in confined and crowded spaces
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2014 |
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| Erschienen: |
2014 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
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| Enthalten in: |
Scientific reports - 4(2014) vom: 06. Mai, Seite 4856 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Goscé, Lara [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 22.10.2015 Date Revised 21.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/srep04856 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM237972204 |
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| 520 | |a 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 | ||
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| 700 | 1 | |a Johansson, Anders |e verfasserin |4 aut | |
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