Mathematical models for respiratory syncytial virus (RSV) transmission
Date
2016
Authors
Hogan, Alexandra
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Abstract
Respiratory syncytial virus (RSV) causes respiratory tract
infections in infants and young children. Almost all children
experience an RSV infection within the first two years of life,
and while mortality due to RSV infection is low in developed
countries, the virus presents a significant burden in Australia
and internationally. In temperate regions, RSV displays strong
seasonal patterns. In Perth, Western Australia, RSV detections
show a distinct biennial cycle, and similar patterns have been
observed in other temperate locations. While there is no licensed
vaccine for RSV, there are several candidates in clinical trials.
Understanding the seasonal patterns of RSV, and developing
mathematical models that capture key transmission
characteristics, can assist with planning the future rollout of
an RSV vaccine.
This thesis focusses on three themes: age structure and immunity;
seasonality and climate; and vaccination. For the first theme, I
present age-structured compartmental mathematical models with
waning immunity and seasonal forcing. I fit these models to RSV
data for Perth and explore the parameter space and bifurcation
structures. The models help explain the different patterns in RSV
detections observed globally. In particular, both the seasonality
and immunity parameters must exceed certain thresholds for the
model to produce biennial patterns, which aligns with observed
data. Further, I identify a ‘window’ of birth rate parameters
that produces biennial patterns, showing that RSV seasonality may
not be only driven by weather and climatic factors as was
previously thought.
The second research theme involves a time series analysis of both
RSV and bronchiolitis data, as approximately 70\% of
bronchiolitis hospitalisations are linked to RSV infection.
First, I identify a clear shift in seasonality for both RSV and
bronchiolitis, from the temperate to tropical regions of Western
Australia. I then apply a mathematical time series analysis
method, complex demodulation, to assess the validity of using
bronchiolitis hospitalisations as a proxy for RSV cases. I find
bronchiolitis and RSV are similar in terms of timing, but that
epidemic magnitudes differ.
To address the third research theme, I adapt the compartmental
model to incorporate a finer age structure, contact patterns and
naturally-derived maternal immunity, to assess the potential
impact of a maternal vaccination strategy for RSV in Perth. I
find that the introduction of a maternal vaccine is unlikely to
alter the regular biennial RSV pattern, but that the vaccine
would be effective in reducing hospitalisations due to RSV in
children younger than six months of age.
This thesis adopts both mathematical modelling and data analysis
approaches to improve our understanding of RSV dynamics.
Developing mathematical models for RSV transmission in the
Australian context allows a better understanding of the relative
importance of age cohorts, immunity, climatic factors, and
demography, in driving different RSV epidemic patterns. Further,
data analysis shows the extent to which bronchiolitis
hospitalisations are representative of RSV detections, and that
different approaches to interventions must be considered in
temperate versus tropical Western Australia. These findings will
be instrumental in planning an effective vaccine rollout strategy
for Western Australia.
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Keywords
respiratory syncytial virus, RSV, mathematical model, infectious disease model, mathematical epidemiology, dynamic model, ordinary differential equation model
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Thesis (PhD)
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