Modelling transmission dynamics and the impact of Modelling
transmission dynamics and the impact of
diff
different vaccination strategies on Respiratory i i
gi
pi
y
Syncytial Virus associated hospitalizations
Syncytial Virus associated hospitalizations
Kinyanjui T.M1*, Munywoki P.K1, Kiti M.C1, Cane P.A2, Medley G.F3, Nokes D.J1,3
1
Centre for Geographic Medicine Research‐Coast, Kenya Medical Research Institute, Kenya
Centre
for Geographic Medicine Research‐Coast Kenya Medical Research Institute Kenya
2 Health Protection Agency, London, UK
Health Protection Agency London UK
3 School of Life Sciences, University of Warwick, Coventry, UK
School of Life Sciences, University of Warwick, Coventry, UK
* Address correspondence to, Email: tkinyanjui@kilifi.kemri-wellcome.org
(TABLE 1) VACCINATION STRATEGIES
RATIONALE
DISCUSSION
• Model
M d l
with
heterogeneous
ith h
t
mixing assumption captures the
age specific profile of hospital
cases with RMSD 7.54 vs 14.61
for homogeneous assumption.
• There is merit in delaying age
at vaccination
i i to 4 months
h if
protective MatAbs last for a
month Benefit is reduced
month.
beyond
y
4 months of age
g at
vaccination.
•For the model assuming
h
heterogeneous
mixing,
i i
the
h
benefit of vaccination appears to
be greater and this may be
attributed
a
bu ed to
o sstronger
o ge indirect
d ec
benefit of vaccination arising
from the nature of the contacts in
the population.
• RSV has been identified as
an iimportant
t th
human pathogen
th
majority of severe disease
occurring in developing
countries ((Nair et al.,, 2010))
• Severe disease associated
with p
primary/early
y
y infection 1-3
months key age group
(H d
l 1979
(Henderson
ett al.,
1979, Gl
Glezen ett
al., 1986)
AGE STRUCTURED MODEL
• No vaccine available for this
target
g g
group.
p Immunogenicity
g
y
vs safety (Karron et al., 2005)
• Explore
p
vaccine effectiveness
outside key target group =>
use mathematical modelling
2
Contin o s Ageing
Continuous
OBJECTIVES
•Model
M d l th
the ttransmission
i i
d namics of RSV using
dynamics
sing a
Realistic Age Structured (RAS)
model
WORK TO BE COMPLETED
• Complete estimating the epi
epiparameters: WAIFW matrix1 (Nokes
DJ., 2008 CID, proposed contact
study in Kilifi)
WAIFW – β(a,a’): Who Acquires Infection
F
From
Wh
Whom matrix.
t i This
Thi matrix
t i describes
d
ib
the probability that an infectious person in
age class a will infect a susceptible
person in age
p
g class a’.
1
•Estimate
E i
d
demographic
hi and
d
epidemiological
id i l i l parameters
t
–
e g WAIFW1
e.g.
•Conduct a sensitivity analysis on
the least certain parameters
The basic reproductive
p
number,, R0, is
defined as the average number of
secondary infections produced when one
infected individual is introduced into a host
population where everyone is susceptible
2
•Investigate the impact of
different vaccination strategies
on iincidence
id
off severe LRTI
(see Table 1)
n
bij (α 0 , j I 0 , j + α 1, j I1, j + α 2 , j I 2 , j )
j =1
Ni
λi = ∑
•Complete the implementation of
the vaccination strategies
• Investigate the role of household
structure
t t
on infection
i f ti dynamics
d
i
using a household stochastic model
•Discrete age classes are considered
•Ageing is continuous
•Infection
disease are dependent
I f ti and
d di
d
d t on
a) Age
b) Number of previous infections
•Develop
D
l an individual-based
i di id l b
d
stochastic
t h ti model
d l tto study
t d
household transmission
ACKNOWLEDGMENTS
Warwick University
Thomas House
Sam Mason
Wellcome Trust support: Grant Ref
No. 084633
PRELIMINARY RESULTS
5
10
15
Age classes
20
25
Fig 1 shows a model fit to age specific RSV hospitalisation
data from Kilifi District Hospital (KDH), Kenya, in children
aged 0 to 5 years with a) a homogeneous and b) a
heterogeneous mixing assumption dynamic models.
References
0.2
0.4
0.6
Vaccination coverage
0.7
0.8
Fig 2 shows the proportion reduction in the number of
hospitalised cases cumulatively over four years as a
function of the vaccination coverage and the month at
vaccination. There is benefit in delaying vaccination up to 4
months
th but
b t reduced
d
d benefit
b
fit iin waiting
iti longer
l
than
th 4 months
th
for higher vaccination coverage.
0 .7 0 .8
0 .9
8
7
0.
0 .9
0 .9
0 .8
0 .7
Ti
Time
series
i off model
d l prediction
di ti ffor effect
ff t off vaccination
i ti
Model data
KDH data
70
No
o of hos
spitalizzations
s per m onth
0 .6
0.
6
0.
0.
0
0 .6
0 .5
0.2
0..1
0 .4
6
60
50
40
30
20
10
2
1
0 .4
0 .3
8
0.8
8
0 .6
0 .2
0 .1
10
4
0.9
0 .8
0 .5
0 .4
5
1
0
12
0 .3
0 .9
2
0.2
0.1
0.3
0 .7
0 .6
5
2
0
0
3
0.
80
14
Mon
nth at vaccin
v
ation
0 .8
0 .7
0 .6
0 .5
6
3
20
0 .4
7
4
40
0 .3
8
0 .2
0 .1
9
0.5
60
10
0. 4
80
0 .1
8
0.
0 .9
12
Mon
nth at vaccin
v
ation
100
R l ti reduction
d ti in
i h
it li d cases - H
t
i i
Relative
hospitalised
Heterogeneous
mixing
16
11
120
18
7
0.
0.
5
0 .4
0 .3
0 .2
13
140
N o of in
ndividu
uals
0 .1
KDH data
Homogeneous model
Heterogeneous model
160
Relative reduction in hospitalised cases - Homogeneous mixing
14
0 .2
A di
Age
distribution
t ib ti off h
hospitalised
it li d cases with
ith model
d l fits
fit
180
KEMRI/Wellcome Unit
Alex
e Maina
a a - Librarian
ba a
RSV team
0.2
0
0 .8
0 . 5 0. 6 . 7
0.4
0.6
Vaccination coverage
g
0.9
0.8
Fig 3 shows the proportion reduction in the number of
hospitalised cases cumulatively over 4 years as a function
of the vaccination coverage and the month at vaccination.
There is benefit in delaying vaccination with greater benefit
compared
d to
t homogeneous
h
model
d l and
d may be
b attributed
tt ib t d to
t
stronger indirect effects.
1
0
2004
2006
2008
2010
2012 2014
Year
2016
2018
2020
2022
Fig 4 shows
Fi
h
the
th h
homogeneous model
d l fit to
t KDH d
data
t ffrom
Nov2004 to Aug2010. Maximum likelihood method was used to
fit the model to data assuming a Poisson distribution
distribution. The fitted
values are: bij = 3.1, amplitude (a) = 0.183 and seasonal offset
value (p) = 0.182. Vaccination is introduced in Feb 2012 at 70%
coverage at 4 months. Note the honeymoon period of about 2
years and the subsequent
y
q
change
g in the epidemiology
p
gy of RSV
after the introduction of a vaccine. Vaccine is assumed to elicit
an immune response equivalent to a natural infection.
Nair H et al. (2010) Global burden of acute lower respiratory infections due to respiratory syncytial virus in young children: a systematic review and meta-analysis. Lancet 375: 1545-1555.
Henderson FW et al (1979)
(19 9) Respiratory-syncytial-virus infections,
f
reinfections
f
and immunity. A prospective, longitudinal study in young children. N Engl J Med 300:
300 530-534.
30 3
Glezen W et al(1986) Risk of primary infection and reinfection with respiratory syncytial virus. American Journal of Diseases of Children 140: 543-546.
Karron RA et al. (2005) Identification of a recombinant live attenuated respiratory syncytial virus vaccine candidate that is highly attenuated in infants. J Infect Dis 191: 1093-1104.
Nokes DJ et al. (2008)
(
) Respiratory
p
y syncytial
y y
virus infection and disease in infants and young
y
g children observed from birth in Kilifi District,, Kenya.
y Clin Infect Dis 46: 50-57.