Probabilistic transmission model for airborne diseases using agent-based modelling
Author
Hennings, Mirco
Term
4. term
Education
Publication year
2022
Submitted on
2022-01-07
Pages
91
Abstract
The COVID-19 pandemic has shown that airborne diseases can pose serious risks to people and society. Early on, many measures were introduced with little evidence, and it remains hard to answer how likely infection is in specific locations. This thesis examines how to represent people’s changing behavior in spaces such as shops, stations, and other public settings to assess infection risk. We use agent-based modeling—a computer simulation in which each person (an “agent”) moves, makes choices, and interacts—and combine it with scientific transmission parameters for airborne diseases. Together, this makes it possible to estimate site-specific probabilities of infection. These insights can guide targeted measures for particular places and help balance necessary protections with keeping society and the economy active. The proposed model can estimate how many occupants in a given setting may become infected and produce a general transmission index for the studied cases. In the future, such indexes based on this framework could support decision-makers.
COVID-19-pandemien har vist, at luftbårne sygdomme kan udgøre en stor risiko for både mennesker og samfund. I begyndelsen blev mange tiltag indført uden sikker viden om deres effekt, og det er stadig svært at svare præcist på, hvor stor smitterisikoen er forskellige steder. Denne afhandling undersøger, hvordan man kan beskrive menneskers skiftende adfærd i rum som butikker, stationer og andre offentlige miljøer for at vurdere smitte. Vi bruger agentbaseret modellering – en computersimulation, hvor hver person (en “agent”) bevæger sig, træffer valg og interagerer – og kombinerer den med videnskabelige transmissionsparametre for luftbårne sygdomme. Tilsammen gør det det muligt at beregne stedsspecifik sandsynlighed for smitte. Den viden kan hjælpe med at designe målrettede tiltag for konkrete steder og finde balancen mellem nødvendige restriktioner og et aktivt samfund og erhvervsliv. Modellen kan bruges til at anslå, hvor mange personer i et givent miljø der kan blive smittet, og til at formulere et generelt transmissionsindeks for de undersøgte anvendelsestilfælde. På længere sigt kan sådanne indeks understøtte beslutningstagere.
[This apstract has been rewritten with the help of AI based on the project's original abstract]
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