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

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

3. 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, (42): p

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

7. Very random

8. ×289 SLIR-model 3 events etc… S L I R Number of infectious
Infectiousness
Incubation time
Recovery time

9. 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

10. 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

11. 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.

12. Run it on a really big PC…

13. You might get something like this

14. Or this

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

16. Much later…
1000 runs
60 days
Average it all out

17. 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?

18. Restricting travel

19. Restricting travel

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

21. Fiddling with g

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