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Workshop on Epidemiology of Infectious Diseases: Emerging Challenges
(4 - 8 January 2010)
Organizing Committee · Visitors and Participants · Overview · Activities · Venue
Chair
- Alex Cook (National University of Singapore)
Members
- Mark Chen (Tan Tock Seng Hospital)
- Chris Gilligan (University of Cambridge)
- Stefan Ma (Ministry of Health)
- Eduardo Massad (University of São Paulo)
- Adrian Röllin (National University of Singapore)
- Yingcun Xia (National University of Singapore)
Contagious diseases are one of the leading causes of death worldwide, resulting in more than 10 million deaths per annum (Lopez et al, 2006).
This is in addition to the social and economic burden of disease in humans (Fonkwo, 2008) and our livestock and agriculture (Agrios, 2005).
The current influenza pandemic demonstrates the speed at which emerging diseases can spread between and within countries. As the most populous continent, Asia plays a key role in the evolution, propagation and dissemination of re-emerging and novel strains of pathogen.
Examples abound: severe acute respiratory syndrome (SARS; Lipsitch et al, 2003; Riley et al, 2003), avian influenza, Streptococcus suis infection (Yu et al, 2006), the 1957 and 1968 influenza pandemics (Kilbourne, 2006) and others all originated in Asia, many as zoonoses.
As a regional travel hub with a large immigrant population and an equatorial climate to which both Aedes aegypti (L) and Ae. albopictus (Skuse) mosquitoes are endemic, Singapore is at particular risk to these and other tropical infectious diseases, such as dengue and Chikungunya, as the 2003 SARS outbreak made evident.
Mathematical modelling is a powerful tool in the fight against infectious disease. Typically, the course of infection is summarised by a compartmental model (Anderson & May, 1991). Historically, between-host transmission was usually assumed to be homogeneous, but recently interest has shifted towards models containing spatial structure as the importance for accounting for heterogeneity became better recognised (e.g. Favier et al, 2005). The act of expressing the salient aspects of within- and between-host dynamics as a mathematical model does much to aid our understanding of the processes underlying an epidemic. However, the utility of modelling goes far beyond that. Models allow us to quantify risk in a way that simply would not be possible otherwise. They permit the prediction of future disease dynamics, both transient and in equilibrium, and consequently allow us to estimate the future economic and health care burden of a disease, as well as providing suggestions for how control should be targeted. Because of the impossibility of carrying out observational studies on the effect of controls during the invasion of a pathogen such as the SARS coronavirus, models are the only viable method of predicting the effect of potential control strategies. In this regard, their worth was amply demonstrated by the involvement in decision making from an early stage of modellers in the controlling the 2001 foot and mouth disease epizootic in Great Britain (Ferguson et al, 2001; Keeling, 2005).
Several challenges have consistently dogged infectious disease modelling. One of the most demanding intellectual issues is parametrising models, in particular the between-host transmission rates. Often, heuristic approaches are devised that do not strictly relate the desired model to the observations. These challenges are being supplemented by emerging challenges to infectious disease modellers. One is technology: on the one hand, the most recent generation of computers allows far more ambitious models to be fit or simulated (Chis Ster & Ferguson, 2007; Cook et al, 2007; Riley, 2007); on the other, they require that modellers learn a whole new set of techniques. Furthermore, the quality and quantity of data being collected, taking advantage of global information and positioning systems, are vastly different to what historically were available, and fully exploiting these is a challenge in itself. Another is globalisation, with the unprecedented motion across borders and the speed at which diseases can be carried long distances, as so spectacularly demonstrated during the SARS outbreak. However, globalisation, too, offers rewards, as it allows networks of researchers to work together more productively. To make models a more useful guide to policy, elements of economics will have to be incorporated (Gersovitz & Hammer, 2003), which again requires new skills to be learnt or collaborations to be fostered. On top of this are the possible effects of climate change on disease, for example by modifying the range and life history of arthropod vectors, which are climate dependent (Gubler et al, 2001; Patz et al, 2005); this risk needs to be assessed and may consequently need to be accounted for in making long-term predictions.
The purposes of the workshop are to highlight on-going work that addresses the emerging challenges in the discipline, including technology, globalisation, economics and climate change. In addition, we aim to enhance the working relationship between researchers and regional policy makers, by deepening their appreciation of modelling and targeting research at their needs.
References:
Agrios, G. N. (2005). Plant pathology, 5 ed. Academic Press.
Anderson, R. M. & R. M. May (1991). Infectious diseases of humans: dynamics and control. Oxford Science Publications.
Chis Ster I. & N. M. Ferguson (2007). Transmission parameters of the 2001 foot and mouth epidemic in Great Britain. PLoS ONE 2:e502.
Cook, A. R., W. Otten, G. Marion, G. J. Gibson & C. A. Gilligan (2007). Estimation of multiple transmission rates for epidemics in heterogeneous populations. Proc Natl Acad Sci USA 104:20392–7.
Favier, C., D. Schmit, C. D. M. Müller-Graf, B. Cazelles, N. Degallier, B. Mondet & M. A. Dubois (2005). Influence of spatial heterogeneity on an emerging infectious disease: the case of dengue epidemics. Proc Roy Soc Lond ser B 272:1171–7.
Ferguson, N. M., C. A. Donnelly & R. M. Anderson (2001). The foot-and-mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science 292:1155–60.
Fonkwo, P. N. (2008). Pricing infectious disease: the economic and health implications of infectious diseases. EMBO reports 9:S13–7.
Gersovitz, M. & J. S. Hammer (2003). Infectious diseases, public policy, and the marriage of economics and epidemiology. World Bank Research Observer 18:129–57.
Gibson, G. J. & E. Renshaw (1998). Estimating parameters in stochastic compartmental models using Markov chain
methods. J IMA Math Appl Med Biol 15:19–40.
Gubler D. J., P. Reiter, K. L. Ebi, W. Yap, R. Nasci & J. A. Patz (2001). Climate variability and change in the United States: potential impacts on vector- and rodent-borne diseases. Environ Health Perspect 109S2:223-33.
Keeling, M. J. (2005). Models of Foot-and-Mouth Disease. Proc Roy Soc Lond ser B 272:1195–202.
Kilbourne, E. D. (2006). Influenza pandemics of the 20th century. Emerging infectious diseases, 12:9–14.
Lipsitch, M. and eleven others. Transmission Dynamics and Control of Severe Acute Respiratory Syndrome. Science 300:1966–1970.
Lopez, A. D., C. D. Mathers, M. Ezzati, D. T. Jamison & C. J. L. Murray (2006). Global burden of disease and risk factors. World Bank Publications.
Patz, J. A., D. Campbell-Lendrum, T. Holloway & J. A. Foley (2005). Impact of regional climate change on human health. Nature 438:310-7.
Riley, S. (2007). Large-scale spatial-transmission models of infectious disease. Science 316:1298–301.
Riley, S. and nineteen others (2003). Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science 300:1961–6.
Yu H. and eighteen others and the Streptococcus suis study groups (2006). Human Streptococcus suis outbreak, Sichuan, China. Emerging infectious diseases, 12:914–20.
Monday, 4 Jan 2010 |
|
08:30am - 08:50am |
Registration |
08:50am - 09:00am |
Opening Remarks |
09:00am - 10:00am |
Incorporating partnership and gap
lengths in STI modeling - why it matters
and how to do it |
10:00am - 10:30am |
--- Coffee Break --- |
10:30am - 11:30am |
Estimating and projecting HIV prevalence
for countries with generalized HIV/AIDS
epidemics |
11:30am - 12:30pm |
HIV and STDs: What can we do using
mathematics? |
12:30pm - 02:00pm |
--- Lunch --- |
02:00pm - 03:00pm |
Agent based simulations for modelling
STD transmission dynamic |
03:00pm - 04:00pm |
On the estimation of R0 from
the initial phase of an outbreak of a
vector-borne infection |
04:00pm - 04:30pm |
--- Coffee Break --- |
Tuesday, 5 Jan 2010 |
|
08:45am - 09:00am |
Registration |
09:00am - 10:00am |
Adopting mathematical modelling for
public health decision making |
10:00am - 10:30am |
--- Coffee Break --- |
10:30am - 11:30am |
Model-based evaluation and
cost-effectiveness analysis of
Methicillin-resistant Staphylococcus
aureus intervention policies |
11:30pm - 02:00pm |
--- Lunch --- |
02:00pm - 03:00pm |
Challenges for national-scale disease
spread and control simulators:
parameterization and economic
considerations |
03:00pm - 04:00pm |
Infectious diseases epidemiology - a
clinicians perspectives on some
unanswered questions |
04:00pm - 04:30pm |
--- Coffee Break --- |
Wednesday, 6 Jan 2010 |
|
08:45am - 09:00am |
Registration |
09:00am - 10:00am |
Assessing the efficacy of hand hygiene &
contact precaution adherence rates on
nosocomial MRSA transmission given
staffing and behavioral constraints
within a surgical intensive care unit |
10:00am - 10:30am |
--- Coffee Break --- |
10:30am - 11:30am |
Social contact network modeling for the
spread of infectious diseases in
Singapore |
11:30am - 02:30pm |
--- Lunch --- |
Thursday, 7 Jan 2010 |
|
08:45am - 09:00am |
Registration |
09:00am - 10:00am |
Statistical modeling of the transmission of infectious diseases |
10:00am - 10:30am |
--- Coffee Break --- |
10:30am - 11:30am |
Studies of pandemic and seasonal
influenza in Hong Kong |
11:30am - 12:30pm |
Predicting the H1N1 outbreak in
Singapore, on-line and in real-time: can
we do the same for dengue? |
12:30pm - 02:00pm |
--- Lunch --- |
02:00pm - 03:00pm |
Robust analyses of epidemic type data,
with application to earthquake and
infectious disease outbreaks |
03:00pm - 04:00pm |
Optimal design of influenza transmission
studies in households |
04:00pm - 04:30pm |
--- Coffee Break --- |
Friday, 8 Jan 2010 |
|
08:45am - 09:00am |
Registration |
09:00am - 10:00am |
On applications of Richards model to
epidemic modeling |
10:00am - 10:30am |
--- Coffee Break --- |
10:30am - 11:30am |
Estimates of pandemic influenza
H1N1-2009 in Singapore |
11:30am - 12:30pm |
Next generation matrices and the type
reproduction number - beyond RO |
12:30pm - 02:00pm |
--- Lunch --- |
02:00pm - 03:00pm |
The relationship between observable and
unobservable quantities in epidemic
modelling |
03:00pm - 04:00pm |
Quantifying effect of responses used in
influenza H1N1 2009 swine flu outbreak
in Australia using an individual-based
model |
04:00pm - 04:30pm |
--- Coffee Break --- |
For attendance at these activities, please complete the online registration form.
The following do not need to register:
- Those invited to participate.
- Those applying for membership with financial support.
Organizing Committee · Visitors and Participants · Overview · Activities · Venue
