Martin Camitz Swedish Institute for Infectious Disease Control, Karolinska Institutet, Stockholm University Martin Camitz

A stochastic model of a moderately contagious disease1 in Sweden and the effect of restricting travel as a control strategy 1Read SARS

A stochastic model etc… About the model Hufnagel et al’s model1 Results 1Hufnagel, L., D. Brockmann, and T. Geisel, Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(42): p. 15124-15129.

Stochasticitics… what? What happens? I + I I R When does this happen?

Very random

×289 SLIR-model 3 events etc… S L I R Number of infectious Infectiousness Incubation time Recovery time

SLIR-model in Solna 3 events S L I R Number of infectious Infectiousness Incubation time Recovery time in Solna Infectious in other municipalities Travel intensity

Intensities Q Q Q S L I R L I R Number of infectious Infectiousness Incubation time Recovery time in Solna Infectious in other municipalities Travel intensity

1. Pick an event 2. Pick a time step Dt 3. Update intensities Stockholm Q L Q I Q R 2. Pick a time step Dt Kalmar Q L Q I Q R Solna Q L Q I 3. Update intensities 4. Repeat from 1.

Run it on a really big PC…

You might get something like this

Or this

Just for sports, let’s not stop this time.

Much later… 1000 runs 60 days Average it all out

Two questions What happens if we restrict travel? Say longer journeys than 50 km or 20 km no longer permitted. What if traveling doesn’t spread SARS as much as we thought?

Restricting travel

Restricting travel

Fiddling with inter-municipal infectiousness Things that probably affect g Total travel intensity Medium of travel Type of transmission Does it matter?

Fiddling with g

Results It works! Travel restrictions slow the spread Lower incidence after 60 days Globally and locally Comparatative results independent of g