20 January 2012

ISDS Member Publication

ISDS member, Dr. Scott McNabb, and ISDS Board member, Dr. John Brownstein, were among the contributing authors of the article "Infectious disease surveillance and modelling across geographic frontiers and scientific specialties" that was recently published in the The Lancet Infectious Diseases. This article discusses the sharing of epidemic intelligence across global frontiers using new technologies and modelling that crosses scientific specialties for improved infectious disease surveillance for mass gatherings.

Read a summary of this article below and the full text in The Lancet to learn more.

Infectious disease surveillance and modelling across geographic frontiers and scientific specialties

Dr Kamran Khan MD a b n Corresponding AuthorEmail AddressScott JN McNabb PhD cProf Ziad A Memish MD d e nRose Eckhardt MA bWei Hu BSc bDavid Kossowsky BA bJennifer Sears BSc bJulien Arino PhD fAnders Johansson PhD g hMaurizio Barbeschi PhD i nBrian McCloskey MDj nBonnie Henry MD k nMartin Cetron MD lJohn S Brownstein PhD m n

Summary: 

Infectious disease surveillance for mass gatherings (MGs) can be directed locally and globally; however, epidemic intelligence from these two levels is not well integrated. Modelling activities related to MGs have historically focused on crowd behaviours around MG focal points and their relation to the safety of attendees. The integration of developments in internet-based global infectious disease surveillance, transportation modelling of populations travelling to and from MGs, mobile phone technology for surveillance during MGs, metapopulation epidemic modelling, and crowd behaviour modelling is important for progress in MG health. Integration of surveillance across geographic frontiers and modelling across scientific specialties could produce the first real-time risk monitoring and assessment platform that could strengthen awareness of global infectious disease threats before, during, and immediately after MGs. An integrated platform of this kind could help identify infectious disease threats of international concern at the earliest stages possible; provide insights into which diseases are most likely to spread into the MG; help with anticipatory surveillance at the MG; enable mathematical modelling to predict the spread of infectious diseases to and from MGs; simulate the effect of public health interventions aimed at different local and global levels; serve as a foundation for scientific research and innovation in MG health; and strengthen engagement between the scientific community and stakeholders at local, national, and global levels.
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