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Geographic and demographic transmission patterns of the 2009 A/H1N1 influenza pandemic in the United States
This thesis describes how transmission of the 2009 A/H1N1 influenza pandemic in the United States varied geographically, with emphasis on population distribution and age structure. This is made possible by the availability of medical claims records maintained in the private sector that capture the weekly incidence of influenza-like illness in 834 US cities. First, a probabilistic method is developed to infer each city's outbreak onset time. This reveals a clear wave-like pattern of transmission originating in the south-eastern US. Then, a mechanistic mathematical model is constructed to describe the between-city transmission of the epidemic. A model selection procedure reveals that transmission to a city is modulated by its population size, surrounding population density, and possibly by students mixing in schools. Geographic variation in transmissibility is explored further by nesting a latent Gaussian process within the mechanistic transmission model, revealing a possible region of elevated transmissibility in the south-eastern US.
Then, using the mechanistic model and a probabilistic back-tracing procedure, the geographic introduction sites (the `transmission hubs') of the outbreak are identified. The transmission hubs of the 2009 pandemic were generally mid-sized cities, contrasting with the conventional perspective that major outbreaks should start in large population centres with high international connectivity. Transmission is traced forward from these hubs to identify `basins of infection', or regions where outbreaks can be attributed with high probability to a particular hub.
The city-level influenza data is also separated into 12 age categories. Techniques adapted from signal processing reveal that school-aged children may have been key drivers of the epidemic.
Finally, to provide a point of comparison, the procedures described above are applied to the 2003-04 and 2007-08 seasonal influenza outbreaks. Since the 2007-08 outbreak featured three antigenically distinct strains of influenza, it is possible to identify which antigenic strains may have been responsible for infecting each transmission hub. These strains are identified using a probabilistic model that is joined with the geographic transmission model, providing a link between population dynamics and molecular surveillance.Gates Cambridge scholarshi
Geographical veracity of indicators from mobile phone data : a study of call detail records data in France
PhD ThesisThe study of mobile phone data opens opportunities in many research domains and for many
applications. One point of critique is that, within current analyses, mobile phone users are
considered uniform and interchangeable. To counter this social atom problem, good research
practice demands an increasing contextualization of research results, for example by confrontation with auxiliary datasets or geographical knowledge. The latter forms the starting point of this
thesis. The main argument is that there exists a spatial knowledge gap when it comes to the use
of indicators derived from mobile phone data. The presented studies assess the geographical
veracity of indicators derived from Call Detail Record (CDR) data and the underlying methods
used. Based on a CDR dataset of almost 18.5 million users in France captured during a 154-day
period in 2007, they show how mobile phone indicators can be constructed for all individual
users using big data technologies. Investigation then is on the performance, sensitivity to user
choices, and error estimations of home detection methods, which form a primordial step for the
aggregation of users in space. Next, a spatial analysis of the popular Mobility Entropy (ME)
indicator is performed, revealing its bias to cell tower density, for which a correction is then
proposed. Ultimately, the relations between mobile phone indicators, indicators from other data
sources and city definitions in France are explored. The main contribution of the thesis is that it
reveals multiple limits of the common practices, results, and interpretations that govern mobile
phone data research. The presented studies challenge the veracity of mobile phone indicators in
different, predominantly geographical, ways and open up discussion on what should be done to
improve trustworthiness