Infectious Disease Surveillance and Modelling Across Geographic Frontiers and Scientific Disciplines

Lancet Infect Dis. 2012 Mar;12(3):222-30. doi: 10.1016/S1473-3099(11)70313-9. Epub 2012 Jan 16.

Infectious Disease Surveillance and Modelling Across Geographic Frontiers and Scientific Disciplines

Khan, K, SJN McNabb, ZA Memish, R Eckhardt, W Hu, D Kossowsky, J Sears, J Arino, A Johansson, M Barbeschi, B McCloskey, B Henry, M Cetron, and J Brownstein

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.

KEYWORDS: infectious disease, modeling