Template-Type: ReDIF-Paper 1.0
Title: People Meet People - A Microlevel Approach to Predicting the Effect of Policies on the Spread of COVID-19
Author-Name: Janos Gabler
Author-Email: 
Author-Name: Tobias Raabe 
Author-Email: 
Author-Name: Klara Röhrl
Author-Email: 

Classification-JEL: C63, I18
Keywords: Covid-19, agent based simulation model, public health measures

Abstract: Governments worldwide have been adopting diverse and nuanced policy measures to
contain the spread of Covid-19. However, epidemiological models usually lack the detailed
representation of human meeting patterns to credibly predict the effects such
policies. We propose a novel simulation-based model to address these shortcomings.
We build on state-of-the-art agent-based simulation models, greatly increasing the
amount of detail and realism with which contacts take place. Firstly, we allow for
different contact types (such as work, school, households or leisure), distinguish recurrent
and non-recurrent contacts and allow the infectiousness of meetings to vary
between contact types. Secondly, we allow agents to seek tests and react to information,
such as experiencing symptoms, receiving a positive test or a known case among
their contacts, by reducing their own contacts. This allows us to model the effects of
a wide array very targeted policies such as split classes, mandatory work from home
schemes or test-and-trace policies. To validate our model, we show that it can predict
the effect of the German November lockdown even if no similar policy has been
observed during the time period that was used to estimate the model parameters.

Note: 
Length: 24
Creation-Date: 2021-02
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp265
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2021_265