A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic
Infectious disease pandemics/epidemics have been serious concerns worldwide. Simulations for public health interventions are practically helpful in assisting policy makers to make wise decisions to control and mitigate the spread of infectious diseases. In this paper, we present our contact network...
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sg-ntu-dr.10356-990702020-05-28T07:17:25Z A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic Lee, Gary Kee Khoon Goh, Rick Siow Mong Fu, Xiuju Zhang, Tianyou Lees, Michael Kwoh, Chee Keong School of Computer Engineering Winter Simulation Conference (2012 : Berlin, Germany) DRNTU::Engineering::Computer science and engineering Infectious disease pandemics/epidemics have been serious concerns worldwide. Simulations for public health interventions are practically helpful in assisting policy makers to make wise decisions to control and mitigate the spread of infectious diseases. In this paper, we present our contact network based simulation model, which is designed to accommodate various 'what-if' scenarios under single and combined interventions. With the incorporation of parallel computing and optimization techniques, our model is able to reflect the dynamics of disease spread in a realistic social contact network based on Singapore city, simulating combined intervention strategies as well as control effect at different levels of a social component. The framework of our model and experimental results show that it is a useful tool for epidemiological study and public health policy planning. 2013-08-02T02:27:46Z 2019-12-06T20:02:53Z 2013-08-02T02:27:46Z 2019-12-06T20:02:53Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99070 http://hdl.handle.net/10220/12819 10.1109/WSC.2012.6465056 en |
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DRNTU::Engineering::Computer science and engineering Lee, Gary Kee Khoon Goh, Rick Siow Mong Fu, Xiuju Zhang, Tianyou Lees, Michael Kwoh, Chee Keong A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
description |
Infectious disease pandemics/epidemics have been serious concerns worldwide. Simulations for public health interventions are practically helpful in assisting policy makers to make wise decisions to control and mitigate the spread of infectious diseases. In this paper, we present our contact network based simulation model, which is designed to accommodate various 'what-if' scenarios under single and combined interventions. With the incorporation of parallel computing and optimization techniques, our model is able to reflect the dynamics of disease spread in a realistic social contact network based on Singapore city, simulating combined intervention strategies as well as control effect at different levels of a social component. The framework of our model and experimental results show that it is a useful tool for epidemiological study and public health policy planning. |
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School of Computer Engineering |
author_facet |
School of Computer Engineering Lee, Gary Kee Khoon Goh, Rick Siow Mong Fu, Xiuju Zhang, Tianyou Lees, Michael Kwoh, Chee Keong |
format |
Conference or Workshop Item |
author |
Lee, Gary Kee Khoon Goh, Rick Siow Mong Fu, Xiuju Zhang, Tianyou Lees, Michael Kwoh, Chee Keong |
author_sort |
Lee, Gary Kee Khoon |
title |
A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
title_short |
A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
title_full |
A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
title_fullStr |
A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
title_full_unstemmed |
A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
title_sort |
contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/99070 http://hdl.handle.net/10220/12819 |
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1681058397864067072 |