Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.

Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) resu...
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Autor*in:	Matt J Keeling [verfasserIn] Thomas House [verfasserIn] Alison J Cooper [verfasserIn] Lorenzo Pellis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Übergeordnetes Werk:	In: PLoS Computational Biology - Public Library of Science (PLoS), 2005, 12(2016), 12, p e1005296
Übergeordnetes Werk:	volume:12 ; year:2016 ; number:12, p e1005296

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1371/journal.pcbi.1005296

Katalog-ID:	DOAJ001871781

Internformat


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allfields	10.1371/journal.pcbi.1005296 doi (DE-627)DOAJ001871781 (DE-599)DOAJ8dd977b964ee49b5ba61b17d60c0f0a0 DE-627 ger DE-627 rakwb eng QH301-705.5 Matt J Keeling verfasserin aut Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections. Biology (General) Thomas House verfasserin aut Alison J Cooper verfasserin aut Lorenzo Pellis verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 12(2016), 12, p e1005296 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:12 year:2016 number:12, p e1005296 https://doi.org/10.1371/journal.pcbi.1005296 kostenfrei https://doaj.org/article/8dd977b964ee49b5ba61b17d60c0f0a0 kostenfrei http://europepmc.org/articles/PMC5283726?pdf=render kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 12, p e1005296
spelling	10.1371/journal.pcbi.1005296 doi (DE-627)DOAJ001871781 (DE-599)DOAJ8dd977b964ee49b5ba61b17d60c0f0a0 DE-627 ger DE-627 rakwb eng QH301-705.5 Matt J Keeling verfasserin aut Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections. Biology (General) Thomas House verfasserin aut Alison J Cooper verfasserin aut Lorenzo Pellis verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 12(2016), 12, p e1005296 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:12 year:2016 number:12, p e1005296 https://doi.org/10.1371/journal.pcbi.1005296 kostenfrei https://doaj.org/article/8dd977b964ee49b5ba61b17d60c0f0a0 kostenfrei http://europepmc.org/articles/PMC5283726?pdf=render kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 12, p e1005296
allfields_unstemmed	10.1371/journal.pcbi.1005296 doi (DE-627)DOAJ001871781 (DE-599)DOAJ8dd977b964ee49b5ba61b17d60c0f0a0 DE-627 ger DE-627 rakwb eng QH301-705.5 Matt J Keeling verfasserin aut Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections. Biology (General) Thomas House verfasserin aut Alison J Cooper verfasserin aut Lorenzo Pellis verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 12(2016), 12, p e1005296 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:12 year:2016 number:12, p e1005296 https://doi.org/10.1371/journal.pcbi.1005296 kostenfrei https://doaj.org/article/8dd977b964ee49b5ba61b17d60c0f0a0 kostenfrei http://europepmc.org/articles/PMC5283726?pdf=render kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 12, p e1005296
allfieldsGer	10.1371/journal.pcbi.1005296 doi (DE-627)DOAJ001871781 (DE-599)DOAJ8dd977b964ee49b5ba61b17d60c0f0a0 DE-627 ger DE-627 rakwb eng QH301-705.5 Matt J Keeling verfasserin aut Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections. Biology (General) Thomas House verfasserin aut Alison J Cooper verfasserin aut Lorenzo Pellis verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 12(2016), 12, p e1005296 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:12 year:2016 number:12, p e1005296 https://doi.org/10.1371/journal.pcbi.1005296 kostenfrei https://doaj.org/article/8dd977b964ee49b5ba61b17d60c0f0a0 kostenfrei http://europepmc.org/articles/PMC5283726?pdf=render kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 12, p e1005296
allfieldsSound	10.1371/journal.pcbi.1005296 doi (DE-627)DOAJ001871781 (DE-599)DOAJ8dd977b964ee49b5ba61b17d60c0f0a0 DE-627 ger DE-627 rakwb eng QH301-705.5 Matt J Keeling verfasserin aut Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections. Biology (General) Thomas House verfasserin aut Alison J Cooper verfasserin aut Lorenzo Pellis verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 12(2016), 12, p e1005296 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:12 year:2016 number:12, p e1005296 https://doi.org/10.1371/journal.pcbi.1005296 kostenfrei https://doaj.org/article/8dd977b964ee49b5ba61b17d60c0f0a0 kostenfrei http://europepmc.org/articles/PMC5283726?pdf=render kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2016 12, p e1005296
language	English
source	In PLoS Computational Biology 12(2016), 12, p e1005296 volume:12 year:2016 number:12, p e1005296
sourceStr	In PLoS Computational Biology 12(2016), 12, p e1005296 volume:12 year:2016 number:12, p e1005296
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Biology (General)
isfreeaccess_bool	true
container_title	PLoS Computational Biology
authorswithroles_txt_mv	Matt J Keeling @@aut@@ Thomas House @@aut@@ Alison J Cooper @@aut@@ Lorenzo Pellis @@aut@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	491436017
id	DOAJ001871781
language_de	englisch
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callnumber-first	Q - Science
author	Matt J Keeling
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topic_title	QH301-705.5 Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks
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title	Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.
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title_sort	systematic approximations to susceptible-infectious-susceptible dynamics on networks
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title_auth	Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.
abstract	Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.
abstractGer	Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.
abstract_unstemmed	Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.
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title_short	Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.
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