Disease spread modeling using contact network
In recent years, due to emergent concerns from the COVID-19 pandemic, there has been an upsurge in interest surrounding the modeling of infectious disease transmission. One widely used approach relies on compartment models, which make assumptions of uniformly mixed populations. While these mod...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2023
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Online Access: | https://hdl.handle.net/10356/171773 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In recent years, due to emergent concerns from the COVID-19 pandemic, there
has been an upsurge in interest surrounding the modeling of infectious disease
transmission. One widely used approach relies on compartment models, which make
assumptions of uniformly mixed populations. While these models offer insights on a
macro level, making them suitable for broad analyses, they can fall short in capturing
the intricate processes of individual-to-individual contacts that drive disease
propagation. To address this gap, our final year project delves into predicting disease
transmission using contact networks, employing the dataset titled “Contact Patterns in
a High School: A comparison between Data Collected Using Wearable Sensors,
Contact Diaries, and Friendship Surveys”. Initially, the project entails an exploratory
data analysis to decipher the publicly available data and to visualize space-time
activity patterns. Subsequent steps involve extracting a contact network from the
dataset to depict disease transmission pathways through vertices and edges. The
research then shifts to an in-depth exploration of the contact network's attributes, it
was discerned that certain nodes, potentially 'super-spreaders', played a
disproportionate role in potential transmission pathways. Our findings underscore the
importance of micro-level analyses for informed intervention strategies. Recognizing
high-risk individuals and understanding their interaction patterns can equip health
authorities with a more granular toolkit, ultimately enabling more targeted and
effective containment measures in outbreak scenarios. |
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