1. Approaches to modeling, parameter estimation, and policy guidance during the endgame Guillaume Chabot-Couture Philip Eckhoff Mike Famulare Hao Hu Hil Lyons Kevin McCarthy Alex Upfill-Brown Bradley Wagner

2. IDM approach to polio research Focus on operational questions first Let the question choose the method Synthesize across disparate data sources and modeling tools – Statistical methods to extract maximal value from GPEI data – Dynamical models to develop understanding of mechanisms – Hybrid models that take advantage of good features of both This talk – Expanded Age Group campaigns – Modeling silent transmission near elimination – Understanding OPV transmission

3. Impact of Expanded Age Group Campaigns for Polio Eradication PLoS ONE 9(12): e113538 Endemic setting (calibrated to Zaria, Nigeria) -Expanded age group (EAG) campaigns are unlikely to significantly improve prospects for polio elimination -Key concern is reaching inaccessible under- immunized populations -Insensitive to waning due to re-exposure from transmission Epidemic in polio-free setting -EAG campaigns can fill immunity gaps left by waning protected fraction mucosal waning rate (years) SIA coverage

4. Has Nigeria eliminated wild poliovirus? From the last case, model to extinction or the next case Dynamical transmission + statistical force of infection = efficient marginalization over uncertainty + near-real-time data assimilation to provide probabilistic forecasts When cases are rare, model the interval between cases. - - - - - - March 31, 2015 Environmental Positive WPV1 probably eliminated from Nigeria WPV3 very likely eradicated cVDPV2 from established lineages likely persists Initialize prevalence based on the interval of the last 2 cases arXiv:1504.02751 Under review

5. Complementary to detailed spatial modeling Calibrated model used to estimate eradication probability given time since last polio paralysis case. Possibility of prior eradication and reimportation in Kano state in 2009/2010, when there was a 20-month silent period. Log 10 (Node Pop) DataSim DataSim McCarthy et al in prep Calibrated spatial model provides a system to study SIA campaign dynamics, cVDPV emergence

6. Spatial model data and calibration Statewide data informs disease model parameters – Incubation and infectious period, seasonal forcing peak and amplitude LGA-level targets inform network-level parameters – Migration rate, heterogeneity in coverage Incremental Mixture Importance Sampling used for parameter exploration/calibration

7. Phylogeography for polio is tough (and are phylodynamics too?) endemic poliopandemic influenza all branches well-resolved ancestor location The timescales of sampled genetic linkage and spatial mixing determine when sequence data is informative. PMID 25733563 On 6 month timescales, dynamics are poorly resolved.

8. Recap so far -EAG campaigns are useful if polio is detected in previously polio-free settings -The effect of mucosal waning at naturally-acquired doses needs to be measured. -Hybrid statistical-dynamical models allow for efficient marginalization over uncertainty -This may be a promising approach for data assimilation and near-term forecasting -Detailed spatial models are useful for understanding determinants of polio persistance -May provide a platform for modeling outbreak response strategies Pieces of the OPV transmission puzzle -The biggest endgame challenge is detecting and preventing cVDPV emergence -Quantitative questions that affect our understanding of cVDPV risk -How transmissible is each OPV strain? -Can we mitigate uncertainties due to ignorance of population structure? -How does genetic reversion affect emergence?

9. Transmissibility of OPV inferred with SIR models R0 – by serology of unvaccinated children Detroit and Houston (Chen et al. 1996) 30% of unvaccinated 12-23 months old are seropositive to type 2 Routine immunization rates known from dose histories Sero-prevalence among zero-dose children increases with R0 Location Age (months) Type 1Type 2Type 3 Detroit12 – 230.69 (0.33 – 1.26)1.42 (0.84 – 2.20)0.42 (0.16 – 0.88) 24 – 350.59 (0.19 – 1.34)1.97 (1.00 – 3.49)1.18 (0.52 – 2.22) Houston12 – 230.28 (0.10 – 0.64)0.92 (0.50 – 1.51)0.59 (0.28 – 1.07) 24 – 350.58 (0.15 – 1.48)1.77 (0.78 – 3.43)0.83 (0.26 – 1.86) Combined12 – 230.45 (0.24 – 0.75)1.13 (0.76 – 1.59)0.48 (0.27 – 0.79) 24 – 350.55 (0.23 – 1.08)1.92 (1.14 – 3.00)1.00 (0.51 – 1.72) Sabin 2 R0 likely greater than 1 R0 – by shedding at RI enrollment Mirpur, Dhaka (PROVDE study) 7% of children shedding OPV2 at 6 weeks of age POL3 coverage is 94% Shedding at enrollment increases with R0 R0 estimates range from 10 to 17 Model predicts ~30% of children get at least one naturally-acquired OPV2 infection during the first year of life

10. Social structure affects meaning of high R0 Vaccine recipient SiblingExtra-familial contact of sibling Low-income setting with low enterovirus background (1-2% of subjects) Primary recipients (n=352) Age 6-18 months 40% prior immunity (mostly IPV) Sibling contacts (n=657) 80% < 5 years old Extra-familial contacts of siblings (n=430) Similar demographics to siblings Inferences about transmissibility (only propagating infectivity uncertainty) High OPV efficacy (81-95%) Many monovalent, historical titers Intra-family transmissibility is high despite evidence for prior immunity R eff = 3.4 (2.4,4.3) Extra-familial transmissibility is lower R eff = 0.85 (0.5,1.3)

11. Challenge to predicting low probability events: network structure Network structure has profound effect on outbreak probability even when mean transmission dynamics are similar. Los Alamos Urban Network mean degree = 16.7 Total population = 100,000 Reproductive number = 1.8 “Power law” network mean degree = 2.0 Epidemic probability = 68% Final epidemic size = 60,000 Epidemic probability = 17% Final epidemic size = 20,000

12. Timescales of OPV reversion Worked with CDC and Nigeria polio Lab to study Sabin-like isolates from AFP surveillance in Nigeria Combined whole-genome sequencing with case history and polio infection model to infer substitution rates of early reversion Improved quantitative estimates of the circulation time required to reach the VDPV threshold Famulare et al under review poster available in meeting materials Potential for silent circulation of VDPV 1 and 3? VDPV case-to-infection ratios may not be wild-type

13. Matlab, Bangladesh OPV2 transmission study tOPV bOPV+1IPV bOPV+2IPV tOPV withdrawal April 2015 tOPV withdrawal Birth cohort and family stool survey 40% of birth cohort and 0-5 year old tOPV challenge Birth cohort and family stool surveys Stool sample at 6, 18 weeks age January 2016 Collaboration with UVA (Mami Tanuichi, Bill Petri) and icddr,b (Rashidul Haque, K Zaman) 1.Measure transmission of OPV2 after tOPV cessation 2.Measure transmission after OPV2 challenge 3.Measure individual protection to type 2 from different RI schedules

14. Environmental surveillance in a low infrastructure setting Testing feasibility and sensitivity of RT-qPCR for poliovirus surveillance in water Correlate ES with respect to stool surveillance -Seasonality of sensitivity -Demographics of sensitive sites -Identify principles of site selection for this type of setting? 1.Map from Google Earth along with GIS information from Matlab used to pick potential sites (201 total from 67 villages) 2.Visit villages as a team (lab technician, porter, and field worker) to survey the sites 3.Site selection based on number of users, surrounding latrines, and etc. 4.Monthly sampling

15. Opinions and questions for the group Quantifying endgame risks and OPV campaigns after cessation requires stacking up a lot of unknowns. We need to respect model misspecification, especially when attempting to predict absolute risks. -How do we mitigate model misspecification risk? -Relative risk ranking? -Efficient marginalization over uncertainty? -Deeper theoretical support for model structures? We need to clearly communicate data gaps and continue to help close them. How do we measure our way out of the risks? -What methodological improvements are required to improve surveillance and the utility of surveillance data?