Modeling antibiotic resistance in populations: from deterministic to stochastic to agent-based models.

1. Modeling antibiotic resistance in populations: from deterministic to stochastic to agent-based models. Laura TEMIME G Thomas, PY Boëlle, D Guillemot L Opatowski, Y Pannet

2. Modeling antibiotic resistance in populations Why model resistance? • To achieve better understanding of underlying processes in resistance selection • To predict future changes • To evaluate control measures such as: • Reduction of antibiotic consumption in the community • Hand washing, systematic isolation or use of rapid diagnostic tests in hospitals

3. Different modeling approaches Available approaches for modeling antibiotic resistance selection in a population: • In large communities (e.g. country): • Compartmental deterministic models  Good prediction of the average behavior • In smaller settings (e.g. schools, hospitals): • Compartmental stochastic models  Information on the variability of processes • Agent-based models  Data on the individual level

4. Presentation outline • Modeling pneumococcal resistance to penicillin using a compartmental model: • Deterministic model • Stochastic model  Several papers between 2003 and 2006 • Modeling the selection and spread of antibiotic resistance in hospital settings: • Individual-based model  Preliminary results, work in progress

5. I- Modeling the selection of pneumococcal resistance to penicillin in France A deterministic model (Temime L, Boëlle PY, Courvalin P, Guillemot D; Emerg Infect Dis, 2003)

6. Context overview • S. pneumoniae: • Human pathogen (otitis, pneumonia, meningitis)  3-5 million deaths / year worldwide • Frequent asymptomatic carriage  Up to 40% carriers among children • Widespread antibiotic resistance  In France over 60% of strains exhibit decreased sensitivity to penicillin  frequently observed multiple resistance

7. A specific resistance mechanism S. pneumoniae resistance to penicillin:  Progressive decrease of sensitivity (MIC)

8. A specific model • Objective = combining two levels for pneumococcal resistance selection: • Intra-individual evolution of strains  reproducing the resistance mechanism • Inter-individual transmission of strains • Model characteristics: • Compartmental deterministic model (partial differential equations) • Progressive increase of resistance levels  Colonized compartments structured by MIC (continuous)

9. A specific model: illustration Unexposed to antibiotics Exposed to antibiotics Genetic events progressive MIC increase

10. 8 days 1 tmt / 2 yrs Model parameters 2.2 months

11. D(d) = (m) = Effects of antibiotic exposure • Colonization may persist with probability: • MIC may increase due to genetic events, according to the law:

12. Validation of model predictions Model predictions CNRP data (87-97) • 1987 : mostly antibiotic sensitive strains • 1997 : bimodal distribution of MICs

13. Applications of this deterministic model • Predictions for N. meningitidis: • Differences in resistance levels of pneumococci and meningococci can be explained by their natural histories of colonization alone • High resistance levels expected in years to come • Pneumococcal conjugate vaccination: • Short-term impact on carriage and resistance • BUT expected re-increase in resistance in the long-term, due to replacement of vaccine strains by non-vaccine strains which will become resistant (Temime L, Boëlle PY, Valleron AJ, Guillemot D; Epid Infect, 2005) (Temime L, Guillemot D, Boëlle PY; Antimicrob Agents Chemother, 2004)

14. II- Modeling the selection of pneumococcal resistance to penicillin A stochastic model (Temime L, Boëlle PY, Courvalin P, Guillemot D; Emerg Infect Dis, 2003) (Temime L, Boëlle PY, Thomas G; Math Pop Studies, 2005)

15. Motivation • Shortcomings of the deterministic model: • Averaged predictions • No information on variability • Identical predictions regardless of population size  Developing a stochastic version of the model will allow: • More realism in the description • Better predictions in small populations • Information on the variability of predicted phenomena

16. General principles • Same compartmental model than in the deterministic setting • But transitions between compartments are considered random  Associated transition probabilities For large population sizes, the deterministic solution approximates the mean stochastic epidemics

17. Results in a town-like community (1000 individuals) For a given MIC: Time before the first emergence of a strain with this MIC Time before 20% of strains will have reached this MIC  Penicillin-resistant pneumococcal strains will emerge on average 20 years after the introduction of penicillin, but it may be 10-30 years.

18. III- Modeling the selection of S. aureus multi-resistance in hospital settings (ICUs) An agent-based model

19. Context overview • Staphylococcus aureus : • Human pathogen (skin infections, septicemia, endocarditis) • 10-40% asymptomatic carriers • Colonization duration?? • Antibiotic resistance : • Widespread penicillin resistance • Methicillin resistance (MRSA) common in hospital settings since the 1960’s (30-50% of all strains) • Emergence of MRSA in the community in recent years

20. Some important questions • What are the determinants for persistence of a staphylococcal strain in a hospital setting? • Why aren’t NO-MRSA successful outside hospitals? • Which context could allow for the successful introduction of CA-MRSA in hospitals? • What would be the consequences?

21. Why an agent-based model? • Individual-based or agent-based modeling has proved useful for: • Modeling epidemic spread in an urban network (Eubank et al., Nature, 2004) or even at a countrywide scale (Longini et al., Am J Epid, 2004) • Simulating healthcare activities in a hospital setting (Boelle et al., Comput Biom Res, 1998) • Modeling pathogen dissemination in an ICU (Hotchkiss et al., Crit Care Med, 2005) and interventions  Allows for more realism and easier description of individual behaviors

22. Model structure: hospital ward (ICU) Patient 1 Patient 2 Patient 3 Patient 4 … Ward corridors / Staff room Doctors Nurses … … 1 2 3 4 1 2 3 4 ROOMS …

23. Model structure: agents and agent characteristics

24. Model structure: agents and agent characteristics (2)  Transmission of colonization through infectious contacts

25. First model outcomes (1) Real-time graphical display of the hospital ward: Follow-up of the geographical spread of micro-organisms

26. First model outcomes (2) Temporal changes in proportions of colonized individuals:

27. First model outcomes (3) Who colonized whom? History of transmission:

28. Perspectives for the ABM • Lots of possible uses: • Educational tool • Assessment of control measures taking into account individual behaviors (non compliance) • Predictions for dominance in a two strain-environment (CA-MRSA and NO-MRSA) • Disadvantages: • Not a mathematical model  no analytical expression available • Costly in simulation time • Large amount of data needed

29. Need for data:complex models require complex data

30. Need for data • Data for model building (parameter values): • Micro-organism characteristics (duration of colonization, invasivity, ...) • Human characteristics (daily activities and contacts, immunity status, ...) • Resistance characteristics (mechanism of emergence, current susceptibility levels, ...) • Data for model validation: • Historical data on emergence and spread of resistant strains in specific settings  Allows comparison with model predictions

31. Specific needs for different kinds of models The amount of required data increases with model complexity: • Compartmental models: • Mean characteristics in the population: duration and frequency of antibiotic exposure, infectious contact rate • Mean characteristics of the micro-organism: duration of colonization, susceptibility to antibiotic exposure • Resistance mechanism characteristics • Agent-based models: • Similar characteristics at the individual level • Supplemental data on individual behaviors

32. Infectious contact rate • Complex parameter which includes: • The frequency of inter-human contacts • The transmissibility of the micro-organism • Estimation strategies: • Not directly observed in populations • Often calibrated to reflect observed colonization • May be estimated using MCMC methods from longitudinal data (Cauchemez et al., BMC Inf Dis, 2006) • Will be measured in hospital settings using “contact tracers” carried by staff and patients (MOSAR project, WP7)

33. Conclusions and perspectives

34. Conclusions • Rising impact of the modeling approach to study antibiotic resistance selection over the last 15 years : • More published models • More cited by a wider audience • Recent developments: more complex models which require more complex data  Antibiotic resistance modeling can only be satisfyingly achieved through a collaboration with microbiologists, physicians, etc.