Literature Searches. Systematic searches of Ovid Medline and EMBASE databases were conducted to identify relevant papers, restricted to papers published from 1996 onwards (for search terms see Appendix). Grey literature searching included Google Scholar. Searches were made of rele- vant government and non-government organisation (NGO) websites, such as CDC, ECDC, WHO, Public Health England (PHE) and relevant mass transport/travel related sites such as IATA; International Civil Aviation Organization (ICAO); and project specific sites – such as AIRSAN and SHIPSAN and other relevant EU projects. Titles and abstracts were searched, and full papers retrieved for those meeting the inclusion criteria. Reference tracking of included papers was conducted and relevant documents from the government/NGO websites listed above retrieved. The surveillance systems and tools identified were categorized according to type, and data extracted to excel for live or dormant systems, but not for obsolete systems (though these were noted and listed in the appendix). For signal detection, the main concepts have been summarised for relevant papers identified in the search.
Literature Searches. We are interested in pandemic potential diseases, particularly pandemic influenza, and the role of the global transportation network in the spread of such diseases. The search terms (which were developed by two individuals) have been chosen to reflect this and are listed in Table 1. Within the model terms, we included the known model types of: metapopulation, agent-based, and network, but also the general terms ‘mathematical’ and ‘simulation’ to capture any other modelling methods. Similarly we searched explicitly for influenza which is the classical example of a pandemic disease. However we also included a more general global disease spread term “disease spread”, along with terms ‘global’ and ‘pandemic’ to pick up any other models. Finally we included terms (with wildcards) to capture the movement of individuals.3 The literature searches yielded 1453 papers across three databases: 332 from Embase (1974-present), 439 from PubMed (1946-present), 682 from Scopus (1970-present). Record titles and abstracts from the three databases were imported into EndNote X7.3.1 and duplicates were identified and removed with the aid of the software’s `remove duplicate’ function. This left 799 records after all duplicates were removed. 1 Mathematical 2 Metapopulation OR meta-population 3 Agent-based 4 Simulation 5 Network 6 #1 OR #2 OR #3 OR #4 OR #5 7 Disease spread 8 Influenza OR flu 9 #7 OR #8 10 Global 11 Pandemic 12 #10 OR #11 13 Travel* 14 Import* 15 Transport* 3 We wish to acknowledge the PHE Knowledge and Library Service for their help and advice regarding scoping study methods. 16 #13 OR #14 OR #15 17 #6 AND #9 AND #12 AND #16
Literature Searches. 4.2.1 Dexcel is responsible for local literature search in the Territory for articles that include reports of suspected adverse drug reactions possibly associated with a Product. Dexcel is subsequently responsible for processing (including requests for additional information from the authors where appropriate) and reporting to the relevant Health Authorities any ICSRs identified where required to do so by current legislation. Such reports identified by Dexcel from local literature search should be forwarded to Company according to the timelines defined in section 5.1.5.
4.2.2 Company is responsible for global literature search.
