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The dynamics underlying global spread of emerging infectious diseases

ckinlam - Posted on 16 December 2015

Project Description: 

Metapopulation models parameterized with worldwide air network (WAN) data have been the mainstream tool for studying global spread of emerging infectious diseases (EIDs) such as pandemic influenza, SARS, and more recently, MERS-CoV and Ebola [1,2]. Despite the computational advances and widespread use, limited studies have been conducted to elucidate the dynamics underlying these models by analytically characterizing how epidemic arrival times (EATs) in different populations (e.g. cities) depend on the epidemiologic parameters and properties of the WAN. In this study, we show that EATs in WAN-based global spread can be analytically calculated with high accuracy from the epidemiologic and network parameters, hence explicitly characterizing their interdependence.

Researcher name: 
Joseph T Wu
Researcher position: 
Associate Professor
Researcher department: 
WHO Collaborating Centre for Infectious Disease Epidemiology and Control - School of Public Health - Li Ka Shing Faculty of Medicine - The University of Hong Kong - Hong Kong Special Administrative Region - China
Researcher email: 
joewu@hku.hk
Researcher name: 
Lin Wang
Researcher position: 
Postdoctoral Fellow
Researcher department: 
WHO Collaborating Centre for Infectious Disease Epidemiology and Control - School of Public Health - Li Ka Shing Faculty of Medicine - The University of Hong Kong - Hong Kong Special Administrative Region - China
Researcher email: 
lwang14@hku.hk
Researcher name: 
CK Lam
Researcher position: 
Information Technology Officer
Researcher department: 
WHO Collaborating Centre for Infectious Disease Epidemiology and Control - School of Public Health - Li Ka Shing Faculty of Medicine - The University of Hong Kong - Hong Kong Special Administrative Region - China
Researcher email: 
ckinlam@hku.hk
Research Project Details
Project Duration: 
Dec/2015 to Nov/2016
Project Significance: 
We aim to elucidate the foundational principle that drives the global spread of emerging infectious diseases. Such theory is vital in guiding future spatial transmission model design and the computational implementation of state-of-the-art global epidemic simulators. We cannot have a big leap in improving model performance without a comprehensive understanding of the dynamics underlying global spread of emerging infectious diseases. With such theory and simulators, we can exactly characterize the spatial transmission trajectories of diseases all over the world. We can better understand the geographic transmission of many emerging or reemerging infectious diseases such as SARS, AH1N1, MERS, and Ebola. Since Hong Kong is an international transportation hub all over the world, we need advanced knowledge to help guide the health-system / public transport system design and allocation. In light of this, our project can have significant practical implications.
Results Achieved: 
In progress
Remarks: 
We sincerely wish to obtain computational support from HPC2015 and Gridpoint, because we need more computational resources to complete our project. The ‘fourday (120 cores)’ account can exactly match our expectation to implement parallel computer simulations with 120 scenarios of epidemic parameters generated via Latin Hypercube Sampling design. Such high-level simulations will be invaluable in consolidating our theoretical models, which will be quite helpful to our final publications on high-ranking top journals.