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Workshop on Epidemiology of Infectious Diseases: Emerging Challenges
(4 - 8 January 2010)

Organizing Committee · Visitors and Participants · Overview · Activities · Venue

 

 Organizing Committee

 

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)

 

 Visitors and Participants

 

 

 Overview

 

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.

 

 Activities

 


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
Mark Chen, Tan Tock Seng Hospital

10:00am - 10:30am

--- Coffee Break ---

10:30am - 11:30am

Estimating and projecting HIV prevalence for countries with generalized HIV/AIDS epidemics
Leontine Alkema, National University of Singapore

11:30am - 12:30pm

HIV and STDs: What can we do using mathematics?
Jie Lou, Shanghai University, China

12:30pm - 02:00pm

--- Lunch ---

02:00pm - 03:00pm

Agent based simulations for modelling STD transmission dynamic
Adrian Röllin, National University of Singapore

03:00pm - 04:00pm

On the estimation of R0 from the initial phase of an outbreak of a vector-borne infection
Eduardo Massad, University of São Paulo, Brazil

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
Vernon Lee, National University of Singapore

10:00am - 10:30am

--- Coffee Break ---

10:30am - 11:30am

Model-based evaluation and cost-effectiveness analysis of Methicillin-resistant Staphylococcus aureus intervention policies
Ben Cooper, Mahidol University, Thailand

11:30pm - 02:00pm

--- Lunch ---

02:00pm - 03:00pm

Challenges for national-scale disease spread and control simulators: parameterization and economic considerations
Roman Carrasco, National University of Singapore

03:00pm - 04:00pm

Infectious diseases epidemiology - a clinicians perspectives on some unanswered questions
Paul Tambyah, National University of Singapore
Julian Tang, National University of Singapore

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
Joc Cing Tay, ROSS Scientific Pte Ltd

10:00am - 10:30am

--- Coffee Break ---

10:30am - 11:30am

Social contact network modeling for the spread of infectious diseases in Singapore
Xiuju Fu, Institute of High Performance Computing

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
Yingcun Xia, National University of Singapore

10:00am - 10:30am

--- Coffee Break ---

10:30am - 11:30am

Studies of pandemic and seasonal influenza in Hong Kong
Ben Cowling, The University of Hong Kong, 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?
Alex Cook, National University of Singapore

12:30pm - 02:00pm

--- Lunch ---

02:00pm - 03:00pm

Robust analyses of epidemic type data, with application to earthquake and infectious disease outbreaks
Jason Fine, University of North Carolina at Chapel Hill, USA

03:00pm - 04:00pm

Optimal design of influenza transmission studies in households
Brendan Klick, University of Hong Kong, Hong Kong

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
Ying-Hen Hsieh, China Medical University, Taiwan

10:00am - 10:30am

--- Coffee Break ---

10:30am - 11:30am

Estimates of pandemic influenza H1N1-2009 in Singapore
Stefan Ma, Ministry of Health

11:30am - 12:30pm

Next generation matrices and the type reproduction number - beyond RO
Mick Roberts, Massey University, New Zealand

12:30pm - 02:00pm

--- Lunch ---

02:00pm - 03:00pm

The relationship between observable and unobservable quantities in epidemic modelling
Hiroshi Nishiura, University of Utrecht, The Netherlands

03:00pm - 04:00pm

Quantifying effect of responses used in influenza H1N1 2009 swine flu outbreak in Australia using an individual-based model
George Milne, The University of Western Australia, Australia

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.


 Venue

 

 

Organizing Committee · Visitors and Participants · Overview · Activities · Venue

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