Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk of resistance

Introduction Modelling antiviral use in a pandemic Effect of treatment on transmission Post-exposure prophylaxis Use in containment at source Uncertainties Antiviral resistance:  Seeded resistance  Evolution of resistance

Modelling approach workplace household elementary school secondary school workplace State-of-the-art large scale simulation (up to 300 million pop.) Individuals reside in households, but go to school or a workplace during the day. Transmission probabilities are specified separately for households and different place types. Local movement/travel : random contact between strangers, at a rate which depends on distance. Air travel incorporated.

Influenza natural history New analysis of best available data on pandemic and inter-pandemic flu. Short incubation period – 1-2 days. People most infectious very soon after symptoms. Days Frequency

Assumptions about antiviral effect Values initially used estimated by Longini et al from analysis of Roche data.  Treatment (or PEP) assumed to reduce infectiousness by 60%, from time treatment starts.  Uninfected individual on prophylaxis has 30% drop in susceptibility (=risk of infection per exposure event).  Prophylaxis reduces chance of becoming a ‘case’ by 65%. Now using updated values, but results v. similar.

Clinical influenza Previous work assumed 50% of infections become ‘clinical cases’ – i.e. have ILI, independent of age. Have also looked at 67% (value used by Longini and others). More important quantity is proportion of infections seeking healthcare – here Longini and Ferguson assumptions more similar (Ferguson assumed 90% cases sought healthcare, Germann assumed 60%). Cases assumed to be 2-fold more infectious than non-ILI- generating infections (assumption based on data from Hayden et al. & Cauchemez et al.). Aetiology of disease complex and variable, even for pandemics. No clear basis to predict age-specific clinical attack rates.

Large urban centres affected first, followed by spread to less densely populated areas. Epidemic only a little slower than GB. A US pandemic Up to 12% absenteeism at peak R 0 =2.0/1.7

Mitigation: case treatment Main effect is to reduce severity of cases, but treatment within 24h of onset can also reduce transmission (reduction the proportion ill from 34% to 28%). 25% stockpile is then just enough, assuming 90% of ‘cases’ receive drug – but demand may be higher. Effect relies on very early treatment – within 24h – since infectiousness peaks soon after symptoms start. 48h delay gives no reduction in transmission and much poorer clinical benefit. So 25% stockpile is bare minimum – could well lead to rationing. No treatment 2 day delay 1 day delay 0 day delay

Household prophylaxis (PEP) Household prophylaxis+ treatment of everyone in house of case, not just case herself Nature paper results: Combined with school closure and rapid case treatment, PEP can reduce clinical case numbers by ~1/3 for R 0 =2– but needs antiviral stockpile of 50% of population. UK now increasing stockpile to >50%, considering role for household PEP.

Varying timing and coverage in PEP Table shows cumulative clinical attack rate over pandemic. Results assume case treatment and prophylaxis of households of treated cases. No NPIs. Even with only 75% coverage and a 2 day delay, PEP can reduce attack rates by 25%. But effect v limited for >2 day delay. Delay to treat (days) % cases detected and treatedR 0 =1.9R 0 =2.4 N/A

Stockpile sizes required for PEP As previous slide, but showing antiviral courses used, as % of population size. No allowance for wastage made here (e.g. due to treating non-flu ILI). Conservatively, need drug for 75% of population to cover all these scenarios and allow for some wastage. Delay to treat (days) % cases detected and treatedR 0 =1.9R 0 =2.4 N/A

Use of AV in containment at source Need to add geographically targeted mass prophylaxis to treatment and close contact PEP to block transmission enough to achieve control. Still also need NPIs (and vaccine also helps). Need a maximum of 3m courses of drug – if you need more then outbreak is too large to be contained. Need to detect outbreak at <50 cases, react to new cases in 2 days. Too intensive to be used except in containment at source.

Uncertainties Nature of virus – modelling assumes next pandemic virus will look like past pandemic viruses – but H5N1 might be different, and the duration and dose of NAI required for treatment may differ. Transmission rates in different settings. (Real-world) effectiveness of drug. Adherence. Behavioural responses to epidemic and other controls. Antiviral resistance. …..

Antiviral resistance Resistance only a major issue during a pandemic if a resistant strain emerges with close to the transmission fitness of wild-type. Current spread of oseltamivir-resistant H1N1 strains demonstrates this is a possibility. But we have no idea of the probability (per treated &/or infected person) of such a strain emerging during a pandemic. So can only look at plausible illustrative scenarios. Two possibilities:  Resistance emerges elsewhere and a mixture of sensitive and resistant strains are seeded into your country.  Resistance emerges for the first time in your country.

Selection of resistance: theoretical worst case Amplification of resistance depends on level and promptness of treatment & prophylaxis. Reduction in attack rate from antivirals also quantifies selection pressure for resistance. If all cases were treated instantaneously, attack rate would be reduced to 24%. Adding 100% prophylaxis would give 16%. But if 1% of infections entering country at start of epidemic are resistant, antiviral effect substantially reduced. Resistance substantially ampilfied, esp. by PEP. Worst-case – 100% of cases get instantaneous treatment or treatment+ household PEP from day 1. No NPI.

Real-world selection pressure for resistance Delays in real-world treatment mean weaker selection, so much less amplification of resistance. Large-scale use of household prophylaxis would amplify resistance more, but effect still v. limited. Final level of resistance can be less than seeded proportion, due to ‘head start’ of sensitive epidemic. Treatment of 60% of cases within 1 day of onset. Treatment starts after 1000 cases in US.

de novo evolution of resistance Assume risk per infected person per day of generating a transmission fit resistant virus of and pessimistic values. In reality evolution of transmissible resistant strain probably requires multiple changes, so this is worst case. Treatment of clinical cases never results in substantial resistance overall. For very pessimistic assumptions, household PEP can strongly select for resistance. Instantaneous treatment of all cases from 1 st case in US, mutation rate. 60% of cases treated in 24h from 1000 th case, mutation rate.

Conclusions Treatment needs to be delivered rapidly to have best direct and indirect effect. Household prophylaxis+treatment on its own can reduce attack rates by 1/3, if delivered ‘rapidly’ to >75% of households of cases. Large scale prophylaxis (i.e. community rather than household), can achieve near-control, but delivery equally challenging. A combination of interventions gives more failsafe policy (e.g. NPIs slow spread of resistance). Antiviral resistance only likely to be a larger problem in the first wave if it emerges very early in the pandemic, with virus being fully fit. If transmissible resistant strains do emerge early, prophylaxis should be used with caution.

Collaborators Christophe Fraser Simon Cauchemez Aronrag Meeyai Don Burke Derek Cummings Steven Riley Sopon Iamsirithaworn RTI Inc NCSA NIH MIDAS programme

Private stockpiles (bought in advance by households) US Govt. initiative to encourage households to stockpile antiviral medkits. Modelling predicts impact of 25% private stockpile on attack rates negligible. Some reduction in demand for public stockpile (25% public stocks might then be enough). Could be huge geographic (income-related) disparities in uptake. The same money far better invested in public stocks – if distribution efficient.