1. Multistrain Epidemic Dynamics on Dynamical Networks
Francesco Pinotti
Ecole Doctorale Pierre Louis de Santé Publique
Institut Pierre Louis d’Epidémiologie et de Santé Publique UMR-S 1136
Supervisors: Pierre Yves Boelle, Chiara Poletto

2. Multistrain Same pathogen multiple interacting strains
S. aureus: MRSA, MSSA, variable resistance profiles
Obadia et al PLoS Comp Biol 2015, Chambers & DeLeo Nature 2009
Influenza: 4 strains, continuous emergence, frequent vaccine mismatch
Bedford et al nature 2015, Nelson PloS 2008
HPV: 40 strains, vaccine for 2
Burchell et al Vaccine 2006, Clifford et al Lancet 2005, Pons-Salort et al Vaccine 2013
S. pneumoniae, Gonorrhea, Malaria, Dengue, …

3. Multistrain spread at population level
Dynamics at population level
Co-circulation
Replenishment
Rapid extinction
Consequences
epidemic risk
outbreak size
emergence of new strains

4. Modelling disease spread: compartmental models
Individuals classified according to health
Susceptible
Infected
Example:
Susceptible-Infected-Susceptible (SIS) model
Compartment transitions:
Infection
Recovery

5. Modelling disease spread: generalization to more strains
Susceptible
Infected
Example: Two strains
Strain 1
Strain 2
Both

6. Modelling disease spread: generalization to more strains
Susceptible
Infected
Example: Two strains
Strain 1
Strain 2
Both
Example:
SIS model with competitive exclusion

7. Modelling disease spread: generalization to more strains
Susceptible
Infected
Example: Two strains
Strain 1
Strain 2
Both
Example:
SIS model with competitive exclusion
Other biological interactions:
Cross-immunity
Enhanced susceptibility

8. Modelling disease spread: contact information
Contacts through which diseases spread:
Face to face
Sexual contact
Physical contact
Credits: sociopatterns.org
High resolution data:
Schools (Vanhems et al PLoS ONE 2013)
Hospitals (Obadia et al PLoS Comp Biol 2015)
Workplaces (Salathe et al PNAS 2011)

9. Modelling disease spreading: contact structure
Population modelled as a network:
Individuals Nodes
Contacts Links
If contact data are resolved in time
Dynamical networks
time

10. The problem How does the dynamics of contacts affect coexistence? Or
Dominance Or
Strains
Population
Co-dominance
Which factors drive the outcome?
Homogeneous mixing: biological mechanisms
Castillo-Chavez et al SIAP 1999, Andreasen et al J Math Biol 1997
Static contacts: biological mechanisms & contact structure
Héfresne-Dufresne & Althouse PNAS 2015, Leventhal et al Nature Comm 2015, Poletto et al PLoS Comp Biol 2013
How does the dynamics of contacts affect coexistence?

11. Case study Close proximity Interactions in a hospital: Carriage data
85025 CPIs
4 months
590 participants
5 wards
Carriage data
S. aureus strains (n = 114)
Weekly sampling
1550 positive swabs
Obadia et al PLoS Comp Biol 2015

12. First step Simulate the spread of two strains in a hospital
Prevalence
Time (hours)
Determine contact properties impacting:
Coexistence time
Prevalence
Coexistence time

13. Next steps and expected results
Numerical simulations
Poletto et al PLoS Comp Biol 2013
Mathematical understanding
Valdano et al Phys Rev X 2015
Analyzing prevalence data
Expected results
1) Biological understanding
Infer
Prevalence data
Strain information
Contacts
Biological interactions
Epidemiological parameters
2) Scenario analysis
Systematic characterization of possible epidemic outcome
Risk of large outbreaks
Risk of new strain emergence