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

Dec 17, 2015

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Page 1: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

Neil Ferguson

Dept. of Infectious Disease EpidemiologyFaculty of Medicine

Imperial College

Antiviral use in a pandemic:predicting impact and the risk of resistance

Page 2: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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

Page 3: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

Page 4: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

0

2

4

6

8

10

12

14

16

0

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

Days

Fre

qu

enc

y

0

0.5

1

1.5

2

0 2 4 6 8 10

Days post infection

Infe

cti

ou

sn

es

s

.

0

1000

2000

3000

4000

5000

Vir

al l

oa

d (

da

ta)

.

Page 5: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

Page 6: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

Page 7: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

• Large urban centres affected first, followed by spread to less densely populated areas. Epidemic only a little slower than GB.

A US pandemic

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

0 30 60 90 120 150 180Day of global outbreak

Da

ily

cas

es

First UScase

Up to 12% absenteeism at peak

R0=2.0/1.7

Page 8: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

0

0.5

1

1.5

2

0 2 4 6 8 10

Days post infection

Infe

cti

ou

sn

es

s

.0

1000

2000

3000

4000

5000

Vir

al l

oa

d (

da

ta)

.

No treatment

2 day delay

1 day delay

0 day delay

0%

10%

20%

30%

40%

Att

ack

rate

R0

Page 9: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

Household prophylaxis (PEP)

• Household prophylaxis+ treatment of everyone in house of case, not just case herself.

• 2006 Nature paper results: Combined with school closure and rapid case treatment, PEP can reduce clinical case numbers by ~1/3 for R0=2– but needs antiviral stockpile of 50% of population.

• UK now increasing stockpile to >50%, considering role for household PEP.

Page 10: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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 treated R0=1.9 R0=2.4

N/A 0 32 39

0 50 23 30

  75 20 27

  90 18 25

1 50 25 32

  75 22 28

  90 20 27

2 50 27 34

  75 24 32

  90 23 30

3 50 28 36

  75 26 34

  90 26 33

Page 11: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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 treated R0=1.9 R0=2.4

N/A 0 0 0

0 50 39 47

  75 48 60

  90 52 66

1 50 39 47

  75 50 60

  90 55 66

2 50 40 48

  75 51 61

  90 56 67

3 50 40 48

  75 52 61

  90 58 67

Page 12: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

Use of AV incontainment 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.

Page 13: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

• …..

Page 14: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

Page 15: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

26.3%28.2%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Treat only Treat+PEP

Clin

ical

att

ack

rate

21%

72%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Treat only Treat+PEP

Prop

orti

on o

f all

case

s in

fect

ed w

ith

resi

stan

t vir

us

Page 16: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

Real-world selection pressurefor 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.

0.9%

1.8%

0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%2.0%

Treat only Treat+PEP

Prop

orti

on o

f all

case

s in

fect

ed w

ith

resi

stan

t vir

us

Page 17: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

0.0035%

0.0094%

0.000%0.001%0.002%0.003%0.004%0.005%0.006%0.007%0.008%0.009%0.010%

Treat only Treat+PEP

Prop

orti

on o

f all

case

s in

fect

ed w

ith

resi

stan

t vi

rus

1%

30%

0%

5%

10%

15%

20%

25%

30%

35%

Treat only Treat+PEP

Prop

orti

on o

f all

case

s in

fect

ed w

ith

resi

stan

t vi

rus

de novo evolution of resistance

• Assume risk per infected person per day of generating a transmission fit resistant virus of 10-4 and10-5 - 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 1st case in US, 10-4 mutation rate.

60% of cases treated in 24h from 1000th case, 10-5 mutation rate.

Page 18: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

Page 19: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

Collaborators

Christophe Fraser Simon Cauchemez

Aronrag Meeyai

Don BurkeDerek Cummings

Steven Riley Sopon Iamsirithaworn

RTI IncNCSA

NIH MIDAS programme

Page 20: Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College Antiviral use in a pandemic: predicting impact and the risk.

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.

0 – 5%5-10%10-15%15-20%20-25%25-30%30-35%35-40%40-45%45-50%50-55%55-60%60%+ (max 67%)