Optimising the surveillance and control of communicable diseases: the effects of population heterogeneity on early outbreak transmission dynamics

Thesis

Mathematical models are increasingly used in epidemiological settings to understand pathogen transmission and to guide the implementation of surveillance and control strategies. In this thesis, we focus on modelling transmission dynamics in the very early stages of an infectious disease outbreak, using mathematical and computational techniques to design control mechanisms that facilitate early case detection and local containment.

When an infectious pathogen first arrives in a susceptible population, the initial imported case(s) may lead to a major disease outbreak, or may instead fade out with only a few infections ever observed. In Part I of this thesis, we use SARS-CoV-2 as a case study to demonstrate how the risk that a major outbreak occurs can be calculated analytically and used as a metric for assessing the effectiveness of control interventions that aim to interrupt transmission. Initially, we consider the impact of transmission occurring from nonsymptomatic infectious individuals, and demonstrate that surveillance targeting individuals who are not showing symptoms can be crucial for outbreak containment. Then, we extend our model to account for age-dependent factors that affect SARS-CoV-2 transmission, including patterns of social contacts, variation in susceptibility to infection, and the likelihood of developing clinical symptoms. Using this age-structured model, we compare the effects of non-pharmaceutical control interventions such as school closures, workplace closures, and social distancing policies, that affect individuals differently according to their age.

For pathogens with the potential to cause a widespread outbreak, early detection through effective surveillance of the susceptible population can be crucial for the success of subsequent control measures. In Part II of this thesis, we turn to the context of plant disease to explore how early detection monitoring for invasive pathogens can be improved. Surveillance for plant pathogens is often hindered by a long incubation period during which plants may be infectious but not displaying visible symptoms. To overcome this, `sentinel' plants - alternative susceptible host species that display visible symptoms of infection more rapidly - could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. Here, we use Xylella fastidiosa infection in Olea europaea (European olive) as a current high-profile case study to inform the construction of a computational model of pathogen transmission and surveillance. Using this model, we demonstrate that including sentinel plants in monitoring programmes could reduce the expected prevalence of infection upon outbreak detection, thereby reducing the cost of post-detection control and increasing the feasibility of local containment and eradication.

Authors: Lovell-Read, F

• DOI: 10.5287/ora-gaak0mp4k
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: mathematical epidemiology; Xylella fastidiosa; early outbreak detection; infectious disease modelling; outbreak risk; COVID-19; disease surveillance
• Subjects: Epidemiology--Mathematical models