THESIS
2021
1 online resource (ix, 50 pages) : illustrations (chiefly color)
Abstract
We explore the structure of COVID-19 infection relation in a group-based approach. In
each study, instead of analysing all patients who fall within a period as other epidemic
models suggest, we only consider patients who present in the same group.
We begin by collecting Hong Kong’s historical COVID-19 data and construct the infection
relationship between patients using directed graphs. Since we restrict the in-degree
to be 1, i.e. each patient is infected by a previous patient, the relation has a random
recursive tree shape. Scale-free power-law has long been used to demonstrate the connectivities
between vertices in a social contact network. We can intuitively find that a lot
of patients do not further contaminate others and only a few patients spread the disease
widely. Therefore, we...[
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We explore the structure of COVID-19 infection relation in a group-based approach. In
each study, instead of analysing all patients who fall within a period as other epidemic
models suggest, we only consider patients who present in the same group.
We begin by collecting Hong Kong’s historical COVID-19 data and construct the infection
relationship between patients using directed graphs. Since we restrict the in-degree
to be 1, i.e. each patient is infected by a previous patient, the relation has a random
recursive tree shape. Scale-free power-law has long been used to demonstrate the connectivities
between vertices in a social contact network. We can intuitively find that a lot
of patients do not further contaminate others and only a few patients spread the disease
widely. Therefore, we use the number of infections of each patient to fit the power-law
distribution and prove the data set follows the power-law distribution.
Referring to the depth of a random graph, we formulate the equations in order to
compute the probability that outbreaks terminate in different layers. Besides, we derive
other equations that determine the probability of different cluster sizes. We show the
theoretical result computed by the above equations gives a similar output with the simulation
result. It allows us to gain more insights to understand the degree and trend of
the outbreak within a group.
We add a chapter which introduces another research topic about the effect on the
service time by the workload in a Chinese Medicine clinic. Following the similar study of
Kc and Terwiesch [1], we show that the rise in instant workload can motivate the workers
which boosts the working efficiency and shortens the consultation time. But a consistent
and intense workload leads to fatigue of labours and reduces the service rate.
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