Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Zika: epidemiology & control
Imperial College London
Neil Ferguson
neil.ferguson@imperial.ac.uk
Timeline
• Apparent gradual spread across Pacific, though phylogeography not
yet certain
(Samantha Lycett, virological.org)
Why flaviviruses are
hard to model
• Acute immunising infections – hard to
measure transmission intensity
• Immune-mediate...
• Secondary cases per case
• Determines age at infection,
attack rate, impact of controls
• Attack rate depends on weakly ...
R0 for Zika
• Hard to estimate:
 Varies spatially & temporally
 Serology hard to interpret
• Zika probably similar to de...
Dynamics of invasion
Why now?
• Pure chance?
• Genetic change?
• El Niño–Southern Oscillation?
 If climate driven, then transmissibility
might...
Disease invasions
• Key – lack of population
immunity
• Speed dependent on R0,
generation time, population
connectivity, s...
What if dengue cross-reacts
with Zika?
• Scenario: immunity
increasing with age - 35
year olds have 50% of
the susceptibil...
How does age of infection
change over time?
• Depends on R0
• All ages equally affected during
initial epidemic
• After in...
Data needs
• Infection attack rates from serological surveys:
 infer R0, per-infection risk of microcephaly,…
 Age strat...
Interventions
Interventions in first wave
• Motivation: reduce attack
rate
• But interventions need
sustained effect
• Benefits of even ...
Persistent vector interventions
• Massive use of RIDL or Wolbachia
could largely prevent exposure
• But needs to happen be...
Vaccine
• If it can be licensed in the next 12-24 months, might still
have an impact in some areas of Latin America
• Impa...
• Very different epidemiology from both Ebola and MERS-CoV – not ‘containable’
• Need R0 estimates to project likely incid...
Upcoming SlideShare
Loading in …5
×

Zika: epidemiology and control

440 views

Published on

Presentation by Professor Neil Ferguson of Imperial College London at the One Health for the Real World: zoonoses, ecosystems and wellbeing symposium, London 17-18 March 2016

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

Zika: epidemiology and control

  1. 1. Zika: epidemiology & control Imperial College London Neil Ferguson neil.ferguson@imperial.ac.uk
  2. 2. Timeline • Apparent gradual spread across Pacific, though phylogeography not yet certain (Samantha Lycett, virological.org)
  3. 3. Why flaviviruses are hard to model • Acute immunising infections – hard to measure transmission intensity • Immune-mediated interactions between flaviviruses • Disease not always apparent • Aedes aegypti population density highly spatiotemporally variable • So transmission dynamics also highly variable
  4. 4. • Secondary cases per case • Determines age at infection, attack rate, impact of controls • Attack rate depends on weakly on transmissibility for R0>2 – implications for control • Endemic age distribution of cases will vary markedly with transmission intensity Importance of R0 0 10 20 30 40 50 60 70 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0 1 2 3 4 5 6 7 Averageageatinfection Lifetimechanceofinfection R0 Risk of infection Age at infection
  5. 5. R0 for Zika • Hard to estimate:  Varies spatially & temporally  Serology hard to interpret • Zika probably similar to dengue:  Kucharski 2016 – Polynesia – R0=1.9-3.1 (always <4)  Nishura 2016 – Yap island – 4.3-5.8  Rodríguez-Barraquer 2016 – suggests dengue and zika transmissibility highly correlated From Johansson et al, Vaccine, 2011 R0 <1 required for elimination, so controls need to reduce R0 by >50%, preferably 80%+ Dengue R0
  6. 6. Dynamics of invasion
  7. 7. Why now? • Pure chance? • Genetic change? • El Niño–Southern Oscillation?  If climate driven, then transmissibility might be less in future years
  8. 8. Disease invasions • Key – lack of population immunity • Speed dependent on R0, generation time, population connectivity, seasonality • Models need to be spatial  Initial wave of transmission will be over within 1-2 years in a single location  But may take up to 5 years to affect whole of Latin America  Initial wave of transmission likely to be followed by 10+ years of v low incidence (due to herd immunity from 1st wave) Simulations show results from simple spatial stochastic model for incidence in total modelled population. Results are illustrative rather than predictive. 0 100 200 300 400 500 600 0 10 20 30 40 50 60 Annualisedweekly incidence/10k Years since introduction Simulated incidence in Latin America peak R0=2.0-4.0 0 20 40 60 80 100 120 140 160 2 4 6 8 10 Reportedcases/100,000 Week (2016) Surveillance - Zika Colombia Neiva Cúcuta Medellín Sincelejo Piedecuesta COLOMBIA
  9. 9. What if dengue cross-reacts with Zika? • Scenario: immunity increasing with age - 35 year olds have 50% of the susceptibility of new- borns • Results in smaller initial wave • Shorter time to become endemic • Enhancement may also facilitate persistence 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 Annualisedweekly incidence/10k Years since introduction Simulated incidence in Latin America peak R0=2.0-4.0
  10. 10. How does age of infection change over time? • Depends on R0 • All ages equally affected during initial epidemic • After initial wave, the mean age of infection falls – older people immune, newborn children susceptible • But for reasonable values of R0, likely than endemic mean age at infection will be around start of child-bearing age range 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 Annualaverageof meanageatinfection Years since introduction low medium high
  11. 11. Data needs • Infection attack rates from serological surveys:  infer R0, per-infection risk of microcephaly,…  Age stratified – infer risk over time &/or by age  Multiple locations – assess geographic heterogeneity  Cohorts – compare seroconversion rates and disease incidence • Need sensitive and specific tests (dengue cross-reactivity an issue) Imai et al, Plos NTD, 2014
  12. 12. Interventions
  13. 13. Interventions in first wave • Motivation: reduce attack rate • But interventions need sustained effect • Benefits of even sustained vector control now are limited unless they reduce R0 below 1 0 10 20 30 40 50 60 2 3 4 5 6 7 8 9 10 11 12 Annualisedweekly incidence/10k Years since introduction No controls With controls Simulated incidence in Latin America (peak R0=1.4-2.2), with 1/3 reduction in mosquito density in Y4 in 40% of continent
  14. 14. Persistent vector interventions • Massive use of RIDL or Wolbachia could largely prevent exposure • But needs to happen before or early in an epidemic to have major impact • Wolbachia potentially more sustainable & affordable than RIDL – only needs to be released once • wMel strain likely to stop transmission for at least ~30 years -40% -20% 0% 20% 1 2 3 4 5 10 15 20 25 30 40 50 60 70 80 90 100 Reductioninde Years after introduction 20% Reductioninde -40% -20% 0% 20% 40% 60% 80% 100% 1 2 3 4 5 10 15 20 25 30 40 50 60 70 80 90 100 Reductionindenguedisease Years after introduction R 60% Reductionindenguedisease 10 15 20 25 30 40 50 60 70 80 90 100 ears after introduction 1.5 2 2.5 3 3.5 4 4.5 5 R0 20% -40% -20% 0% 20% 40% 60% 80% 100% 1 2 3 4 5 10 15 20 25 30 40 50 60 70 80 90 100 Reductionindenguedisease Years after introduction 1.5 2 2.5 3 3.5 4 4.5 5 R0 40% 1.5 2 2.5 3 3.5 4 4.5 5 R0 60% -40% -20% 0% 20% 40% 60% 80% 100% Reductionindenguedisease 1.5 2 2.5 3 3.5 4 4.5 5 R0 80% Pessimistic projections of wMel impact on endemic dengue
  15. 15. Vaccine • If it can be licensed in the next 12-24 months, might still have an impact in some areas of Latin America • Impact likely to be much lower outside that timeframe • Trial design will be challenging:  Sites with recent transmission are unlikely to see much more for 10+ years  Hard to predict which sites will be affected next year, with what attack rate  Microcephaly endpoints difficult
  16. 16. • Very different epidemiology from both Ebola and MERS-CoV – not ‘containable’ • Need R0 estimates to project likely incidence trends in the next few years • Serological surveys will allow transmissibility, microcephaly risk to be assessed • First wave of transmission in Latin America will mostly be over within ~3 years. • Herd immunity means there likely 10+ year gap before transmission restarts • Interventions need to be effective, sustained & timely to have substantial effect • RIDL and Wolbachia both have potential • Vaccine efficacy trials will need to be innovative Conclusions

×