Open or closed?
Agent-based Simulations of SARS-CoV-2 Prevention Measures in Austrian Schools

Graz University of Technology     |     Complexity Science Hub Vienna

Jana Lasser     |     jana.lasser@tugraz.at     |     @janalasser


slides available at
https://janalasser.at/talks/school_covid_talk_short/

Motivation

Im autum 2020, keeping schools open or closing them has become a very ideological discussion in Austria:

... opening schools is no problem since children are less susceptible to the virus ...

... children get infected in schools and spread the virus to their families ...

... wearing masks and frequently airing the rooms is harmful for children ...

... keeping schools closed is harmful for children ...

Scientific questions

We aimed to provide scientific evidence to enable informed decisions:

Can outbreaks in schools be controlled with non-pharmaceutical intervention measures at all?

What measures work best?

How many measures are necessary?

Are children less infectious?

Modelling SARS-CoV-2 in schools

Modelling SARS-CoV-2 in schools

Modelling SARS-CoV-2 in schools

Modelling SARS-CoV-2 in schools

Model of the infection

Model of the infection

Model of the infection

Model of the infection

Model of the infection

Model of the infection

Model of the school

Model of the school

Model of the school

Model of the school

Model of the school

Model of the school

Calibration

Calibration

Calibration

Calibration

Calibration

Remaining free parameters:

How much smaller is the transmission risks for
K1 contacts?

How much smaller is the transmission risks for
K2 contacts?

How much smaller is the transmission risk for children?

Calibration


Data from AGES cluster tracing.

Calibration – Results

A K1 contact is 15% less likely than a household contact to transmit an infection.

A K2 contact is 25% less likely than a household contact to transmit an infection.

Children are 2% less likely per year younger than 18 to transmit an infection.

Recap

We have a calibrated model

of different school types

that we can use to test interventions.

Interventions

Interventions

Interventions

Interventions

Interventions

Single measures

Single measures

Single measures

Single measures

Single measures

Single measures

Single measures

Single measures

Measure combinations

Measure combinations

Simulation package "small comunity SEIRX" (Python):
https://pypi.org/project/scseirx

Application to schools: https://github.com/JanaLasser/school_SEIRX

Publication preprint:
https://doi.org/10.1101/2021.04.13.21255320

Supplement: Sensitivity analysis

Efficiency of individual measures is very uncertain.

Class size reduction: How well does it work?

Masks: Are they worn correctly?

Ventilation: How efficient is it really?

Preventive testing: How many participate voluntarily?

Test technology: How sensitive are the tests?

Virus: Mutants with higher infectivity?

Sensitivity analysis

Linear decrease in sensitivity leads to exponential increase in cases.

Scenario 1: Conservative assumptions about measure implementation

  • AG test sensitivity: 40% (instead of 100%)
  • 50% participation in voluntary tests (instead of 100%)
  • Only 30% of students stay at home (instead of 50%)
  • Room ventilation reduces transmission risk by 20% (instead of 64%)
  • Masks reduce transmission risk by 40% [20%] for exhaling [inhaling] (instead of 50% [30%]).

    • Scenario 2: Mutant with increased transmissibility

  • Keep optimistic assumptions about measure implementation.
  • Increase the base transmission risk by 50%.

    • Assessment observables

    Baseline: scenario with literature values

    X: X-fold increase of mean outbreak size over baseline

    R: Number of transmissions from the index case

    Single measures

    Primary schools seem to be safe, other school types depend on the scenario.

    Single measures

    Primary schools seem to be safe, other school types depend on the scenario.

    Single measures

    Primary schools seem to be safe, other school types depend on the scenario.