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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/171773 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-171773 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1717732023-12-15T15:38:06Z Disease spread modeling using contact network Wu, JunYan Cai Wentong School of Computer Science and Engineering ASWTCAI@ntu.edu.sg Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity 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. Bachelor of Engineering (Computer Science) 2023-11-08T00:38:10Z 2023-11-08T00:38:10Z 2023 Final Year Project (FYP) Wu, J. (2023). Disease spread modeling using contact network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171773 https://hdl.handle.net/10356/171773 en application/pdf application/vnd.ms-powerpoint Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity |
spellingShingle |
Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity Wu, JunYan Disease spread modeling using contact network |
description |
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. |
author2 |
Cai Wentong |
author_facet |
Cai Wentong Wu, JunYan |
format |
Final Year Project |
author |
Wu, JunYan |
author_sort |
Wu, JunYan |
title |
Disease spread modeling using contact network |
title_short |
Disease spread modeling using contact network |
title_full |
Disease spread modeling using contact network |
title_fullStr |
Disease spread modeling using contact network |
title_full_unstemmed |
Disease spread modeling using contact network |
title_sort |
disease spread modeling using contact network |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171773 |
_version_ |
1787136583539884032 |