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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317552

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of disease import, of changed activity participation rates over time (coming from mobility data), of masks, of indoors vs.\ outdoors leisure activities, and of contact tracing. Results show that the model is able to credibly track the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model clearly shows the effects of contact reductions, school closures/vacations, or the effect of moving leisure activities from outdoors to indoors in fall. Sensitivity tests show that all ingredients of the model are necessary to track the current infection dynamics. One interesting result from the mobility data is that behavioral changes of the population mostly happened \textit{before} the government-initiated so-called contact ban came into effect. Similarly, people started drifting back to their normal activity patterns \emph{before} the government officially reduced the contact ban. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, consequences of wearing masks in certain situations, or contact tracing.

2.
PLoS One ; 16(10): e0259037, 2021.
Article in English | MEDLINE | ID: covidwho-1496524

ABSTRACT

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


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods , Berlin , COVID-19/metabolism , Cell Phone/trends , Computer Simulation , Germany , Hand Disinfection/trends , Humans , Masks/trends , Models, Theoretical , Physical Distancing , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Systems Analysis
3.
Physica A: Statistical Mechanics and its Applications ; : 126322, 2021.
Article in English | ScienceDirect | ID: covidwho-1351808

ABSTRACT

We present an agent-based epidemiological model that is based on an agent-based model for traffic and mobility. The model consists of individual agents that follow individual daily activity plans, which include, for each activity, locations, start times, and end times. Evidently, one can place a virus spreading dynamic on top of this, by infecting one or more agents, and then track the resulting virus dynamics through the model. Normally, the model is used to investigate non-pharmaceutical interventions. In the present paper, we undertake steps to better understand the infection graph. It becomes clear that the typical infection graph representation that connects individual people is an even more expensive representation than our original, already expensive data-driven mobility model. We then undertake first steps towards analysing the model with respect to a possible percolation transition.

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