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Human mobility and spatial models for infectious disease


Type

Thesis

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Abstract

Human mobility is an important determinant for the spatial spread of human infectious diseases such as influenza but obtaining human mobility datasets has historically been difficult. This thesis investigates two ways to represent human mobility in spatial metapopulation models for the spread of influenza in the US and UK – using gravity models with data-based distance metrics and using survey mobility data from the BBC Pandemic project and the 2011 UK census. Our metapopulation models describe the spread of influenza on a network of geographically segregated subpopulations that make up the whole population. Interactions between subpopulations are characterised by the human mobility proxies, while homogeneous mixing is assumed within subpopulations. The choice of subpopulations can therefore potentially have a large influence on the model output, and so this thesis also considers how this choice of spatial scale for the aggregation of the human mobility data and for the model can affect the epidemic dynamics produced.

Chapter 2 investigates the use of data-based distance metrics in a gravity model framework fit to influenza spread in the US. Given that people do not move via straight lines, we consider driving distance by road and driving time as alternative distance metrics to great-circle distance. Gravity models are fit to outbreak onset dates in the US for the 2009 A/H1N1pdm influenza pandemic and the 2003/04 and 2007/08 influenza seasons, derived from influenza-like-illness medical claims timeseries at the scale of 3-digit ZIP codes (ZIPs). Driving distance and time are found to give better gravity model fits than great-circle distance to this data and simulations highlight spatial differences in the spread predicted by the different distance metrics.

Chapter 3 explores the effect that spatial scale of the data and model has on the results in the previous chapter and considers two spatial scales in addition to ZIPs: sectional centre facilities (SCFs) and states. We compare the results from using different scales for obtaining outbreak onset dates from the influenza-like-illness timeseries, model fitting to the outbreak onset dates, and simulating from the model parameters. The better modelling performance of driving distance and driving time compared to great-circle distance persisted at the SCF level but not at the state level.

Chapter 4 describes the England mobility data from the BBC Pandemic citizen science project that recorded location data of participants via a mobile phone app in 2017-2018. Compared to the most widely used open-source England human mobility data in the last decade, the 2011 census commuter workflow matrices, the BBC location data is more recent and records the movement of a wider range of people and trips but is relatively sparser. To compare the two datasets, we aggregate the BBC data into origin-destination matrices and fit competing destination models, an extension of the gravity model, to both BBC and census mobility data at three spatial scales: local authority districts (LADs), upper tier local authorities (UTLAs) and regions. Model preference was similar between datasets and scales, but parameter estimates differed.

Chapter 5 uses the fitted mobility matrices in the previous chapter in a compartmental metapopulation model for influenza disease spread in England to compare simulated output from using the BBC and census mobility datasets. The resulting simulated epidemic dynamics are evaluated at the three scales (LADs, UTLAs, regions).

Additionally, Chapter 6 presents a retrospective analysis of another source of survey data – for coughs, colds, and influenza-like illness in the University of Cambridge from 2007-2008. This self-reported data from university students and staff is one of the most detailed datasets of infectious respiratory disease in UK universities pre-COVID-19. Although a simple survey that comes with biases, it provides insights into risk factors for infectious disease in the relatively closed environment of a university and suggests ways in which future surveys could be carried out.

Description

Date

2022-04-01

Advisors

Gog, Julia

Keywords

citizen science, epidemiology, gravity model, human mobility, infectious disease, influenza, influenza-like-illness, mathematical modelling, mobile phone mobility data, spatial model, statistical modelling, survey

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Sponsorship
EPSRC (1936275)
Engineering and Physical Sciences Research Council (1936275)
EPSRC [grant number EP/N509620/1]
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