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-mediated interactions

between flaviviruses

• Disease not always apparent

• Aedes aegypti population density

highly spatiotemporally variable

• So transmission dynamics also highly

variable

• 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

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Risk of infection

Age at infection

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

Dynamics of invasion

Why now?

• Pure chance?

• Genetic change?

• El Niño–Southern Oscillation?

If climate driven, then transmissibility

might be less in future years

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.

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Simulated incidence in

Latin America

peak R0=2.0-4.0

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Surveillance - Zika Colombia

Neiva

Cúcuta

Medellín

Sincelejo

Piedecuesta

COLOMBIA

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

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Years since introduction

Simulated incidence in

Latin America

peak R0=2.0-4.0

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 range0

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low

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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

Interventions

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

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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

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

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Years after introduction

1.522.533.544.55

R0

20%-40%

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Years after introduction

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Years after introduction

1.522.533.544.55

R0

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Years after introduction

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R0

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Years after introduction

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R0

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Years after introduction

1.522.533.544.55

R0

40%

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Years after introduction

1.522.533.544.55

R0

60%-40%

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Years after introduction

1.522.533.544.55

R0

80%

Pessimistic projections of

wMel impact on endemic

dengue

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

• 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