Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771157
Title: Commercial airline travel and the international spread of emerging infectious diseases
Author: Meslé, Margaux Marie Isabelle
ISNI:       0000 0004 7656 8110
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2018
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Abstract:
A total of 1.186 billion international airline arrivals were recorded globally in 2015 alone, a 4.6% increase from 2014 (Glaesser et al., 2017). As airplanes now fly very long distances at greater speeds, a passenger is likely to travel while incubating a pathogen and may only become ill once at their destination. In the 21st century alone, a number of pathogens have been transported in this way, causing epidemics (Cholera in Haiti, 2010) and pandemics (Influenza A H1N1, 2009). The aims of this thesis were to understand what previously purchased airline data represents in terms of passenger movement and whether this is a useful and/or accurate tool to use to predict the international spread of human infectious diseases. A systematic literature review first analysed what airline data was most often used by mathematical modellers to determine the international spread of human infectious diseases and how well the data sources were reported. From there, the OAG airline data was extensively described and validated against independent and open access data sets. With a better understanding of the airline data, the author modelled which regions posed varied risks of chikungunya and dengue infection for UK passengers compared to the local populations, by combining endemic and imported number of cases to the airline data. Finally, the author conducted an analysis regarding which countries posed a higher risk for the initial spread of a pandemic by deriving their global connectivity from the airline data and using the level of healthcare provided from two indices. Both parameters were given equal importance by providing equal weightings before ranking each country by proximity to a fictitious 'Worst Case Scenario'. It was determined that commercial (closed access) airline data was most often used by the modelling community and that the reporting of sources used did not often allow for independent validation of a group's work. As a result, a framework was developed for researchers to report specific aspects of the data set, such as date range included, any manipulation and date of collection. When describing the airline data, clear seasonal trends were apparent, and countries such as the United States and China contribute large numbers of passengers to the network. Additionally, the data are sold as highly accurate airline only data, but was identified as also containing land and sea transportation. When validated, the OAG data showed good agreement with the other data sets used such as from the United Kingdom's Office for National Statistics and the United States' Department of Transport. From the modelling chapters, some regions, such as the Caribbean, proved less dangerous for UK airline passengers in terms in chikungunya and dengue infection compared to the local populations whereas regions such as Lower South America were more dangerous for dengue specifically, for UK passengers. Using two independent indices and the same connectivity data, the author showed that certain countries exhibited the potential greatest risk of international pandemic spread, whereas countries with recent pandemic emergence, such as Brazil and Mexico, showed lower potential risk. Future perspectives of this work include taking the global connectivity and healthcare chapter further by including within-country data. Additionally, the creation of an open-access data set combining detailed airline travel and passenger epidemiology that all research groups could use is an important continuation of this work.
Supervisor: Christley, Rob ; Vivancos, Roberto ; Hall, Ian ; Leach, Steve ; Read, Jonathan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.771157  DOI:
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