The aim of our infectious disease research programme is
to increase understanding of the epidemiology of infections
which pose a substantial public health
threat so that interventions against those infections
can be optimized. We conduct large-scale
field studies of community disease
transmission, and we conduct more
theoretical studies using
techniques borrowed from
classical statistics, mathematical biology
and operations
research. Recently, our main pathogen
focus has been on SARS and influenza. We
also have
active projects looking at tuberculosis,
scarlet fever, and hand, foot and
mouth disease. In addition, we maintain some methodological themes in
our work such as the development of
approaches to
statistical inference using transmission
models. We have strong international
collaborative links as part of the Harvard
Center for Communicable Disease
Dynamics.
We strongly believe that the best evidence to support the improvement of
interventions against infectious disease comes from interdisciplinary projects in which excellent
field and laboratory studies are developed
alongside state-of-the-art quantitative
techniques. Infectious disease
epidemiology at the HKU School of Public
Health is supported by a commissioned
grant from the Research Fund for the
Control of Infectious Diseases.
Influenza
Previous research from our group has shown that human influenza causes more than
one
thousand deaths and many
thousands of hospitalisations every year locally and is therefore one of the most important
infectious diseases in Hong Kong. Furthermore, the prospect of a new influenza pandemic is troubling
given the frightening reality of previous pandemics which have been responsible for the deaths of
many millions of people worldwide. While many key questions about the emergence and spread of
influenza viruses and how they cause
disease remain unanswered, the University
Grants Committee of Hong Kong
supports an Area of Excellence research program entitled
Control of Pandemic
and Inter-Pandemic Influenza to build infrastructure and facilitate continued world-class
research.
Over the past 5 years
we have conducted a series of controlled
trials and observational studies of
influenza transmission in households and
schools. These studies have improved our
understanding of the transmission dynamics
of influenza and the effectiveness of
pharmaceutical and non-pharmaceutical interventions.
Using state-of-the-art mathematical modelling techniques, we have studied how
public health interventions might reduce the impact of a pandemic, how to optimise the use of a pre-pandemic vaccine,
and how to hedge against the risk of drug-induced antiviral resistance in anticipation of large-scale antiviral intervention.
SARS
The Department of Community Medicine was at the forefront of the response
to the 2003 outbreak of severe acute respiratory syndrome (SARS). With our team's academic
input, the government quickly set up an epidemiologic database to facilitate rapid sharing
of information between the hospitals, police, government and advisors, and this 'SARSid'
database was a major factor in the rapid response to and control of the SARS outbreak. Working
collaboratively with colleagues at Imperial College, our group was the first to report on the
epidemiological characteristics of SARS and to have
modelled
its transmission dynamics. In the most detailed epidemiologic postmortem of
the epidemic to date, we reported on
the largest consecutive case-series of
SARS patients.
Theoretical epidemiology of infectious diseases
In addition to our
applied work we also recognise the
need to improve epidemiological practice.
We continue to contribute to the theoretical foundations
of infectious disease epidemiology by
employing various types of mathematical
models. Our modeling studies focus on the following
areas: (i)
the transmission
potential of an infectious disease, (ii)
the
severity of an epidemic or an
infection, (iii) analyzing epidemiological
impact of an intervention, (iv) prevalence
estimation, projection & forecasting and
assessment of validity and predictability,
and (v) mathematical and statistical
models accounting for multi-state,
multi-host, multi-strain and multi-layer
structures with particular emphasis on
their implications to statistical analysis
of infectious disease data. We consider
methods of (i), (ii) and (iv) based on
various sources of data, contributing to
epidemiological study design and real-time
assessment during the course of an
epidemic. To make appropriate
interpretations from empirical data,
a
mathematical formulation of the underlying
dynamics is required, and
interpretations of epidemiological data as
well as the
likelihood functions for
statistical inference can be explicitly
derived from these modeling approaches. We are
particularly interested in the definition,
computation and estimation of various
types of reproduction numbers and
population impact of interventions during
non-linear phases of an epidemic. To comply
with statistical needs during the course
of an epidemic, we employ stochastic
models including non-homogeneous
stochastic processes and multivariate and
multi-state modeling approaches.
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