When and why direct transmission models can be used for environmentally persistent pathogens
Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmissio...
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description | Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration. |
doi_str_mv | 10.1371/journal.pcbi.1009652 |
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Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009652</identifier><identifier>PMID: 34851954</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animals ; Bayes Theorem ; Biology and Life Sciences ; Communicable Diseases - physiopathology ; Communicable Diseases - transmission ; Communicable diseases in animals ; Computational Biology - methods ; Computer Simulation ; Disease Outbreaks ; Disease transmission ; Endemic Diseases ; Environment ; Environmental aspects ; Epidemics ; Epidemiologic methods ; Epidemiological Models ; Humans ; Medical research ; Medicine and Health Sciences ; Medicine, Experimental ; Models, Biological ; Models, Theoretical ; Monte Carlo Method ; Pathogenic microorganisms ; Physical Sciences ; Probability ; Research and Analysis Methods ; Risk factors ; Stochastic Processes</subject><ispartof>PLoS computational biology, 2021-12, Vol.17 (12), p.e1009652-e1009652</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Benson et al 2021 Benson et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4582-98f7d13e5cecfc6cda4f73ac10ed5a3ea4a03df542e0e5508e3313007a9f19f3</citedby><cites>FETCH-LOGICAL-c4582-98f7d13e5cecfc6cda4f73ac10ed5a3ea4a03df542e0e5508e3313007a9f19f3</cites><orcidid>0000-0003-2874-037X ; 0000-0002-0454-9338 ; 0000-0002-9117-7041 ; 0000-0003-1468-5867</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668103/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668103/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2915,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34851954$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Struchiner, Claudio José</contributor><creatorcontrib>Benson, Lee</creatorcontrib><creatorcontrib>Davidson, Ross S</creatorcontrib><creatorcontrib>Green, Darren M</creatorcontrib><creatorcontrib>Hoyle, Andrew</creatorcontrib><creatorcontrib>Hutchings, Mike R</creatorcontrib><creatorcontrib>Marion, Glenn</creatorcontrib><title>When and why direct transmission models can be used for environmentally persistent pathogens</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.</description><subject>Animals</subject><subject>Bayes Theorem</subject><subject>Biology and Life Sciences</subject><subject>Communicable Diseases - physiopathology</subject><subject>Communicable Diseases - transmission</subject><subject>Communicable diseases in animals</subject><subject>Computational Biology - methods</subject><subject>Computer Simulation</subject><subject>Disease Outbreaks</subject><subject>Disease transmission</subject><subject>Endemic Diseases</subject><subject>Environment</subject><subject>Environmental aspects</subject><subject>Epidemics</subject><subject>Epidemiologic methods</subject><subject>Epidemiological Models</subject><subject>Humans</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Medicine, Experimental</subject><subject>Models, Biological</subject><subject>Models, Theoretical</subject><subject>Monte Carlo Method</subject><subject>Pathogenic microorganisms</subject><subject>Physical Sciences</subject><subject>Probability</subject><subject>Research and Analysis Methods</subject><subject>Risk factors</subject><subject>Stochastic Processes</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqVkl9rFDEUxQdRbK1-A5GAL_Vh12Qymcm8CKX4p1AUtOCLEO4mN7spmWRMZqr77c2ya-mCL5KH3Jv8ziG5nKp6yeiS8Y69vY1zCuCXo165JaO0b0X9qDplQvBFx4V8_KA-qZ7lfEtpKfv2aXXCGylYL5rT6sf3DQYCwZBfmy0xLqGeyJQg5MHl7GIgQzToM9EQyArJnNEQGxPBcOdSDAOGCbzfkhFTdnkqLRlh2sQ1hvy8emLBZ3xx2M-qmw_vby4_La6_fLy6vLhe6EbIetFL2xnGUWjUVrfaQGM7DppRNAI4QgOUGyuaGikKQSVyzjilHfSW9ZafVe_2tuO8GtDo8oYEXo3JDZC2KoJTxzfBbdQ63inZtpJRXgzODwYp_pwxT6p8XqP3EDDOWdUtFW2ZHqsL-nqPrsGjcsHG4qh3uLpopex5z9u-UMt_UGUZHJyOAa0r50eCN0eCwkz4e1rDnLO6-vb1P9jPx2yzZ3WKOSe091NhVO1ipA4xUrsYqUOMiuzVw4nei_7mhv8BIITHgg</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Benson, Lee</creator><creator>Davidson, Ross S</creator><creator>Green, Darren M</creator><creator>Hoyle, Andrew</creator><creator>Hutchings, Mike R</creator><creator>Marion, Glenn</creator><general>Public Library of Science</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2874-037X</orcidid><orcidid>https://orcid.org/0000-0002-0454-9338</orcidid><orcidid>https://orcid.org/0000-0002-9117-7041</orcidid><orcidid>https://orcid.org/0000-0003-1468-5867</orcidid></search><sort><creationdate>202112</creationdate><title>When and why direct transmission models can be used for environmentally persistent pathogens</title><author>Benson, Lee ; Davidson, Ross S ; Green, Darren M ; Hoyle, Andrew ; Hutchings, Mike R ; Marion, Glenn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4582-98f7d13e5cecfc6cda4f73ac10ed5a3ea4a03df542e0e5508e3313007a9f19f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Animals</topic><topic>Bayes Theorem</topic><topic>Biology and Life Sciences</topic><topic>Communicable Diseases - physiopathology</topic><topic>Communicable Diseases - transmission</topic><topic>Communicable diseases in animals</topic><topic>Computational Biology - methods</topic><topic>Computer Simulation</topic><topic>Disease Outbreaks</topic><topic>Disease transmission</topic><topic>Endemic Diseases</topic><topic>Environment</topic><topic>Environmental aspects</topic><topic>Epidemics</topic><topic>Epidemiologic methods</topic><topic>Epidemiological Models</topic><topic>Humans</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Medicine, Experimental</topic><topic>Models, Biological</topic><topic>Models, Theoretical</topic><topic>Monte Carlo Method</topic><topic>Pathogenic microorganisms</topic><topic>Physical Sciences</topic><topic>Probability</topic><topic>Research and Analysis Methods</topic><topic>Risk factors</topic><topic>Stochastic Processes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Benson, Lee</creatorcontrib><creatorcontrib>Davidson, Ross S</creatorcontrib><creatorcontrib>Green, Darren M</creatorcontrib><creatorcontrib>Hoyle, Andrew</creatorcontrib><creatorcontrib>Hutchings, Mike R</creatorcontrib><creatorcontrib>Marion, Glenn</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Benson, Lee</au><au>Davidson, Ross S</au><au>Green, Darren M</au><au>Hoyle, Andrew</au><au>Hutchings, Mike R</au><au>Marion, Glenn</au><au>Struchiner, Claudio José</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>When and why direct transmission models can be used for environmentally persistent pathogens</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2021-12</date><risdate>2021</risdate><volume>17</volume><issue>12</issue><spage>e1009652</spage><epage>e1009652</epage><pages>e1009652-e1009652</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34851954</pmid><doi>10.1371/journal.pcbi.1009652</doi><orcidid>https://orcid.org/0000-0003-2874-037X</orcidid><orcidid>https://orcid.org/0000-0002-0454-9338</orcidid><orcidid>https://orcid.org/0000-0002-9117-7041</orcidid><orcidid>https://orcid.org/0000-0003-1468-5867</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Animals Bayes Theorem Biology and Life Sciences Communicable Diseases - physiopathology Communicable Diseases - transmission Communicable diseases in animals Computational Biology - methods Computer Simulation Disease Outbreaks Disease transmission Endemic Diseases Environment Environmental aspects Epidemics Epidemiologic methods Epidemiological Models Humans Medical research Medicine and Health Sciences Medicine, Experimental Models, Biological Models, Theoretical Monte Carlo Method Pathogenic microorganisms Physical Sciences Probability Research and Analysis Methods Risk factors Stochastic Processes |
title | When and why direct transmission models can be used for environmentally persistent pathogens |
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