Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

• DOI: 10.1371/journal.pone.0259037
• Publication Date: 2021-10-28T17:36:08Z
• Authors: Sebastian A. Müller; Michael Balmer; William Charlton; Ricardo Ewert; Andreas Neumann; Christian Rakow; Tilmann Schlenther; Kai Nagel
• Keywords: Contact tracing; Mobile phone

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example COVID-19. This paper presents an approach that combines well-established from transportation modelling uses person-centric data-driven human mobility with mechanistic infection model disease progression model. The includes consequences different room sizes, air exchange rates, import, changed activity participation rates over time (coming data), masks, indoors vs. outdoors leisure activities, contact tracing. It is validated against dynamics in Berlin (Germany). can be contributions types time. predicts effects reductions, school closures/vacations, or effect moving activities fall, thus able quantitatively interventions. shown these best given additive changes reproduction number R. also explains why reductions have decreasing marginal returns, i.e. first 50% considerably more than second 50%. Our work shows possible build detailed epidemiological microscopic models relatively quickly. They investigate mechanical aspects dynamics, such transmission political decisions via behavior infections, lockdown measures, wearing masks certain situations. results inform decisions.