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Project

Point of care gathering and real-time utilization of smartphone generated movement traces of patients for infectious disease containment in the community.

In this PhD thesis, we applied different methods in clinical and epidemiological studies to understand the factors that contributed to respiratory pathogen transmission. These insights could, in turn, improve pathogen surveillance and interruption of transmission.

 

In one chapter, we compared SARS-CoV-2 shedding in different respiratory samples and analytical tests – nasopharyngeal (NP) quantitative polymerase chain reaction (qPCR), NP rapid antigen tests (RAT) and exhaled breath (EB) qPCR – and between different SARS-CoV-2 viral variants (Alpha and Omicron BA.1). We showed that EB shedding, as determined by qPCR, had a distinct shedding pattern characterised by high sensitivity in early infection and low sensitivity in late infection, compatible with a contagiousness test. Shedding was similar across virus variants.

 

In another chapter, we looked at the merits and limitations of qPCR on ambient air sampling as a non-invasive, scalable surveillance tool for infectious diseases in general and respiratory diseases in particular, as a tool to provide insight into (airborne) transmission patterns, and to evaluate transmission reduction efforts. We detected high rates of respiratory pathogen positivity, by multiplex qPCR, in community settings. We saw clear trends in the detection rates throughout the sampling period and across age groups. Also, the influence of indoor air quality on pathogen detection rates was very clear. A second study demonstrated high virus detection rates, by PCR, of mpox (previously monkeypox) in a sexual health clinic.

 

In a third chapter, we used contact tracing data gathered as part of a testing and tracing program

for Leuven students during COVID-19 to determine risk factors for pathogen transmission

and evaluate the effectiveness of different contact tracing strategies. In a first large cohort

study, we provided the first empirical evidence for the efficiency of backward contact tracing to interrupt SARS-CoV-2 transmission. In a second study focused on SARS-CoV-2 transmission risk in

student residences, we showed that the built environment (a high number of individuals sharing sanitary facilities) and behavioral factors (the recent occurrence of a social gathering) could significantly increase the risk transmission. In a third study, we used individual-level

data on the use of the Belgian digital contact tracing app, the sharing of an infection

by a newly diagnosed case, and the receipt of an exposure notification by their manually

traced contacts to empirically evaluate the comprehensiveness of digital proximity tracing

(DPT). We showed that the overall comprehensiveness of DPT was very low, that the infection risk in digitally traced contacts was lower than that of conventionally traced non-app users, and that DPT was not instantaneous. These results highlighted major limitations of a digital proximity tracing

system based on the dominant Google-Apple Exposure Notificaiton (GAEN) framework.

 

Collectively, these chapters demonstrate how we were able to generate some interesting

insights into respiratory pathogen transmission, surveillance and mitigation.

Date:6 Aug 2020 →  Today
Keywords:Mobility traces, spatial data science, geographical information system, infectious disease control
Disciplines:Data mining, Visual data analysis, Geospatial information systems, Epidemiology, Infectious diseases
Project type:PhD project