The use of human mobility estimates in mathematical models of infectious disease spread
File(s)
Author(s)
Wardle, Jack
Type
Thesis
Abstract
Reliable estimates of human mobility are important for modelling the spatial spread of infectious diseases and guiding control efforts. However, human mobility data are often unavailable at the necessary temporal or spatial resolutions, leading to the use of proxies based on data from other countries or past periods. This thesis evaluates the impact of using such proxies in spatial epidemic models.
Subnational mobility data are unavailable for many African countries. As a result, proxies are generated by fitting mobility models (e.g. gravity or radiation models) to data from nearby countries. This thesis first assesses how reliance on such proxies can affect model-based predictions of within-country epidemic spread in the absence of adequate empirical data.
This thesis also examines the use of flight passenger data for modelling the international spread of infectious diseases. In the early stages of outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common, but are typically based on historical data. I explored the validity of this implicit assumption that travel behaviour is unaffected by epidemic events by exploring trends in flight passenger volumes over time and the effects of previous epidemics. I then conducted an epidemic simulation study to compare the performance of spatial models based on historical flight passenger data with models based on contemporary passenger data.
Finally, I developed a framework for modelling the infectious disease risks posed by the international movements of large numbers of people to attend mass gathering events. Using the Hajj as a case study, I examine the impact of mobility proxies on model predictions of epidemiological risks and the cost-effectiveness of control strategies.
This work provides lessons for the use of human movement estimates in future infectious disease outbreak modelling and helps identify priority areas for improved data collection.
Subnational mobility data are unavailable for many African countries. As a result, proxies are generated by fitting mobility models (e.g. gravity or radiation models) to data from nearby countries. This thesis first assesses how reliance on such proxies can affect model-based predictions of within-country epidemic spread in the absence of adequate empirical data.
This thesis also examines the use of flight passenger data for modelling the international spread of infectious diseases. In the early stages of outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common, but are typically based on historical data. I explored the validity of this implicit assumption that travel behaviour is unaffected by epidemic events by exploring trends in flight passenger volumes over time and the effects of previous epidemics. I then conducted an epidemic simulation study to compare the performance of spatial models based on historical flight passenger data with models based on contemporary passenger data.
Finally, I developed a framework for modelling the infectious disease risks posed by the international movements of large numbers of people to attend mass gathering events. Using the Hajj as a case study, I examine the impact of mobility proxies on model predictions of epidemiological risks and the cost-effectiveness of control strategies.
This work provides lessons for the use of human movement estimates in future infectious disease outbreak modelling and helps identify priority areas for improved data collection.
Version
Open Access
Date Issued
2025-01-24
Date Awarded
01/09/2025
License URL
Advisor
Bhatia, Sangeeta
Cori, Anne
Hauck, Katharina
Nouvellet, Pierre
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)