A coupled hidden Markov model for disease interactions

To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to in...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of the Royal Statistical Society Ročník 62; číslo 4; s. 609 - 627
Hlavní autoři:	Sherlock, Chris, Xifara, Tatiana, Telfer, Sandra, Begon, Mike
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Blackwell Publishing Ltd 01.08.2013 John Wiley & Sons Ltd Oxford University Press
Témata:	Adaptive Markov chain Monte Carlo sampling Applied statistics Bartonella Covariance Datasets Discrete time Disease Disease models Diseases Economic theory Forward-backward algorithm Gibbs sampler Hidden Markov models Immune response Infections Inference Logistic regression Logistics Longitudinal studies Markov analysis Markov chains Markov models Markovian processes Mathematical models Medical research Monte Carlo simulation Original Parasites Population (statistical) Probability Public health Regression analysis Samples Sampling Statistical analysis Studies Transition probabilities Voles Zoonosis Gibbs sampler Zoonosis Adaptive Markov chain Monte Carlo sampling Hidden Markov models Forward–backward algorithm
ISSN:	0035-9254, 1467-9876
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites.
AbstractList	Summary To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. Reprinted by permission of Blackwell Publishers To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. [PUBLICATION ABSTRACT] To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites.To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites.
Author	Begon, Mike Xifara, Tatiana Telfer, Sandra Sherlock, Chris
Author_xml	– sequence: 1 givenname: Chris surname: Sherlock fullname: Sherlock, Chris organization: Lancaster University, UK – sequence: 2 givenname: Tatiana surname: Xifara fullname: Xifara, Tatiana email: t.xifara@lancaster.ac.uk organization: Lancaster University, UK – sequence: 3 givenname: Sandra surname: Telfer fullname: Telfer, Sandra organization: University of Aberdeen, UK – sequence: 4 givenname: Mike surname: Begon fullname: Begon, Mike organization: University of Liverpool, UK
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24223436$$D View this record in MEDLINE/PubMed
BookMark	eNqNkktv1DAUhS1URKeFDXtQJDYVUopfie0NUhl1pkilSLTA0vLYN9RDJh7spNB_j4e0o1Ih4G68ON89vq89tNOFDhB6SvAhyfEqpmQPCcWkeoAmhNeiVFLUO2iCMatKRSu-i_ZSWuIcBPNHaJdyShln9QTVR4UNw7oFV1x656Ar3pn4NVwVq-CgLZoQC-cTmASF73qIxvY-dOkxetiYNsGTm3cffZwdX0xPytP387fTo9PS1lhUJak4JWTBMZaMKe64c4Q4IbhdEGZxFlUjleENZ841klMw0lLsAEjDHTRsH70efdfDYgXOQtdH0-p19CsTr3UwXv-udP5SfwlXmknClKiywcGNQQzfBki9XvlkoW1NB2FImkhaV5IpSv6NcqqkZLL6D1dO8v-1ZCKjL-6hyzDELg9N5woVFTJ7Zur53T63Dd4uKgMvR8DGkFKEZosQrDdXoDdXoH9dQYbxPdj63mwWl2fk2z-nkDHlu2_h-i_m-sP5-fQ259mYs0x9iHdqFoJIobJejrpPPfzY6vm-dC2YqPTns7meXZydzN98YnrGfgKtFNs2
CitedBy_id	crossref_primary_10_1016_j_ijpara_2017_11_006 crossref_primary_10_1111_ele_13610 crossref_primary_10_1016_j_jmva_2015_04_002 crossref_primary_10_1007_s10182_021_00395_8 crossref_primary_10_1109_TAI_2022_3227222 crossref_primary_10_1017_S0031182014000171 crossref_primary_10_1186_s13071_015_1167_9 crossref_primary_10_1109_TSP_2020_3025522 crossref_primary_10_1016_j_jmp_2024_102884 crossref_primary_10_1017_S0031182023000677 crossref_primary_10_1088_1742_6596_1490_1_012017 crossref_primary_10_1111_stan_12133 crossref_primary_10_1080_01621459_2023_2263202 crossref_primary_10_1111_1365_2656_13869 crossref_primary_10_7717_peerj_3254 crossref_primary_10_1080_10618600_2019_1654880 crossref_primary_10_1109_TPAMI_2023_3244130 crossref_primary_10_1515_ijb_2018_0023 crossref_primary_10_1177_1471082X20956423
Cites_doi	10.3201/eid1004.030455 10.1016/S0021-9975(97)80041-2 10.1214/ss/1177011136 10.1017/S0950268899002423 10.1016/0304-4076(95)01770-4 10.1080/00949659908811984 10.1198/016214502753479464 10.1089/vbz.2004.4.285 10.1111/j.0006-341X.2000.00733.x 10.1109/5.18626 10.1201/9781420011180 10.1126/science.1190333 10.1214/ss/1015346320 10.1109/WMVC.2007.12 10.1128/AEM.00625-08 10.1111/j.1467-9868.2006.00566.x 10.1023/A:1007649326333 10.1111/j.1365-2656.2011.01893.x 10.1017/S095026880100526X 10.1023/A:1008938201645 10.1128/AEM.02203-10 10.1016/j.epidem.2008.10.001 10.1007/s00442-009-1495-6 10.1111/j.1541-0420.2005.00318.x 10.1017/S0031182006001624 10.1128/JCM.42.7.3164-3168.2004 10.1198/1061860032030 10.1016/0167-7152(93)90127-5 10.1017/S0031182008000395 10.1214/aoms/1177697196 10.1049/ip-smt:20000851 10.3150/08-BEJ176 10.1093/biomet/82.4.711 10.1201/9781420010893
ContentType	Journal Article
Copyright	Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd. 2013 Royal Statistical Society Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd 2013
Copyright_xml	– notice: Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd. – notice: 2013 Royal Statistical Society – notice: Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd – notice: Copyright © 2013 The Royal Statistical Society and John Wiley & Sons Ltd 2013
DBID	BSCLL 24P AAYXX CITATION NPM 7SC 8BJ 8FD FQK JBE JQ2 L7M L~C L~D 7X8 5PM
DOI	10.1111/rssc.12015
DatabaseName	Istex Wiley Online Library Open Access CrossRef PubMed Computer and Information Systems Abstracts International Bibliography of the Social Sciences (IBSS) Technology Research Database International Bibliography of the Social Sciences International Bibliography of the Social Sciences ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef PubMed International Bibliography of the Social Sciences (IBSS) Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	PubMed International Bibliography of the Social Sciences (IBSS) CrossRef International Bibliography of the Social Sciences (IBSS) MEDLINE - Academic Computer and Information Systems Abstracts
Database_xml	– sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Statistics Computer Science Public Health
EISSN	1467-9876
EndPage	627
ExternalDocumentID	PMC3813975 3016686801 24223436 10_1111_rssc_12015 RSSC12015 24771879 ark_67375_WNG_FTNHGBV3_F
Genre	article Journal Article Feature
GrantInformation_xml	– fundername: Wellcome Trust grantid: 095171
GroupedDBID	-~X .3N .4S .DC .GA .Y3 05W 10A 1OC 1OL 29L 2AX 3-9 31~ 33P 3R3 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5HH 5LA 66C 7PT 8-0 8-1 8-3 8UM 8VB 930 A03 AAESR AAEVG AAMMB AANHP AAONW AARHZ AASGY AAUAY AAWIL AAXRX AAZKR ABAWQ ABBHK ABCQN ABCSF ABCUV ABDFA ABEJV ABEML ABFAN ABIVO ABLJU ABPFR ABPQH ABPTD ABWST ABXSQ ABYWD ACAHQ ACBWZ ACCZN ACFRR ACGFS ACHJO ACIWK ACMTB ACNCT ACPOU ACRPL ACSCC ACTMH ACUBG ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADODI ADOZA ADQBN ADRDM ADULT ADVEK ADZMN AEFGJ AEGXH AEIMD AEMOZ AEUPB AFBPY AFEBI AFGKR AFVYC AFXHP AFZJQ AGLNM AGQPQ AGXDD AHQJS AIDQK AIDYY AIHAF AIURR AJAOE AJNCP AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALRMG AMBMR AMVHM AMYDB ANFBD ARCSS ASPBG AS~ ATGXG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BCRHZ BDRZF BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG CO8 COF D-E DCZOG DPXWK DQDLB DR2 DRFUL DRSTM DSRWC EBA EBO EBR EBS EBU ECEWR EDO EJD EMK F00 F5P FEDTE FVMVE G-S G.N GODZA H.T H.X HF~ HQ6 HVGLF HZI HZ~ H~9 IHE IPSME IX1 J0M JAAYA JAS JBMMH JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NF~ NU- O66 O9- OIG OJZSN P2W P2X P4D PQQKQ PZZ Q.N Q11 QB0 QWB R.K RJQFR RNS ROL ROX RX1 SA0 SUPJJ TH9 TUS U5U UAP UB1 W8V W99 WBKPD WH7 WIH WIK WOHZO WQJ WYISQ XBAML XG1 YF5 ZGI ZL0 ZZTAW ~IA ~WT ALUQN 1OB 24P AAHHS ABYAD ACCFJ ACTWD AEEZP AELPN AEQDE AEUQT AFPWT AIWBW AJBDE JSODD AAYXX CITATION O8X NPM 7SC 8BJ 8FD FQK JBE JQ2 L7M L~C L~D 7X8 5PM
ID	FETCH-LOGICAL-c6075-154211b40083394d4dd11d774cb13c04219f89a4f43ddf842ea8c20dee1f4def3
IEDL.DBID	24P
ISICitedReferencesCount	24
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000321706600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0035-9254
IngestDate	Tue Nov 04 01:33:34 EST 2025 Wed Oct 01 13:13:04 EDT 2025 Sun Nov 09 12:15:30 EST 2025 Sat Sep 27 21:44:29 EDT 2025 Mon Nov 10 21:41:28 EST 2025 Mon Jul 21 06:00:54 EDT 2025 Tue Nov 18 21:05:27 EST 2025 Sat Nov 29 02:38:22 EST 2025 Wed Jan 22 16:34:50 EST 2025 Thu Jul 03 21:14:35 EDT 2025 Tue Nov 11 03:33:13 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Keywords	Gibbs sampler Zoonosis Adaptive Markov chain Monte Carlo sampling Hidden Markov models Forward–backward algorithm
Language	English
License	https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c6075-154211b40083394d4dd11d774cb13c04219f89a4f43ddf842ea8c20dee1f4def3
Notes	ArticleID:RSSC12015 istex:E9DDF63D59E18627355CBDF2B82397491A6478AD ark:/67375/WNG-FTNHGBV3-F Reuse of this article is permitted in accordance with the terms and conditions set out at http://wileyonline library.com/onlineopen#OnlineOpen__Terms . SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
OpenAccessLink	https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frssc.12015
PMID	24223436
PQID	1399278883
PQPubID	32577
PageCount	19
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_3813975 proquest_miscellaneous_1826583921 proquest_miscellaneous_1429883855 proquest_miscellaneous_1418136837 proquest_journals_1399278883 pubmed_primary_24223436 crossref_primary_10_1111_rssc_12015 crossref_citationtrail_10_1111_rssc_12015 wiley_primary_10_1111_rssc_12015_RSSC12015 jstor_primary_24771879 istex_primary_ark_67375_WNG_FTNHGBV3_F
PublicationCentury	2000
PublicationDate	August 2013
PublicationDateYYYYMMDD	2013-08-01
PublicationDate_xml	– month: 08 year: 2013 text: August 2013
PublicationDecade	2010
PublicationPlace	England
PublicationPlace_xml	– name: England – name: Oxford
PublicationTitle	Journal of the Royal Statistical Society
PublicationTitleAlternate	J. R. Stat. Soc. C
PublicationYear	2013
Publisher	Blackwell Publishing Ltd John Wiley & Sons Ltd Oxford University Press
Publisher_xml	– name: Blackwell Publishing Ltd – name: John Wiley & Sons Ltd – name: Oxford University Press
References	Robert, C. P., Celeux, G. and Diebolt, J. (1993) Bayesian estimation of hidden Markov chains: a stochastic implementation. Statist. Probab. Lett., 16, 77-83. Chantrey, J., Meyer, H., Baxby, D., Begon, M., Bown, K. J., Hazel, S. M., Jones, T., Montgomery, W. I. and Bennett, M. (1999) Cowpox: reservoir hosts and geographic range. Epidem. Infectn, 122, 455-460. Daniels, M. J. and Hogan, J. W. (2008) Missing Data in Longitudinal Data: Strategies for Bayesian Modelling and Sensitivity Analysis. Boca Raton: Chapman and Hall-CRC. Bown, K. J., Bennett, M. and Begon, M. (2004) Flea-borne Bartonella grahamii and Bartonella taylorii in Bank Voles. Emergng Infect. Dis., 10, 684-687. Burthe, S. J., Lambin, X., Telfer, S., Douglas, A., Beldomenico, P., Smith, A. and Begon, M. (2009) Individual growth rates in natural field voles, Microtus agrestis, populations exhibiting cyclic population dynamics. Oecologia, 162, 653-661. Scott, S. L. (2002) Bayesian methods for hidden Markov models: recursive computing in the 21th century. J. Am. Statist. Ass., 97, 337-351. Bai, Y., Calisher, C. H., Kosoy, M. Y., Root, J. J. and Doty, J. B. (2011) Persistent infection or successive reinfection of deer mice with Bartonella vinsonii subsp. arupensis. Appl. Environ. Microbiol., 77, 1728-1731. Green, P. J. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732. Bown, K. J., Lambin, X., Telford, G. R., Ogden, N. H., Telfer, S., Woldehiwet, Z. and Birtles, R. J. (2008) Relative importance of Ixodes ricinus and Ixodes trianguliceps as vectors for Anaplasma phagocytophilum and Babesia microti in field vole (Microtus agrestis) populations. Appl. Environ. Microbiol., 74, 7118-7125. Telfer, S., Lambin, X., Birtles, R., Beldomenico, P., Burthe, S. J., Paterson, S. and Begon, M. (2010) Species interactions in a parasite community drive infection risk in a wildlife population. Science, 330, 243-246. Guihenneuc-Jouyaux, C., Richardson, S. and Longini, I. M. (2000) Modelling markers of disease progression by a hidden Markov process: application to characterising CD4 cell decline. Biometrics, 56, 733-741. Baum, I. E., Petrie, Y., Soules, G. and Weiss, N. (1970) A maximisation technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist., 41, 164-171. Sherlock, C. and Roberts, G. (2009) Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets. Bernoulli, 15, 774-798. Lachish, S., Knowles, S. C. L., Alves, R., Wood, M. J. and Sheldon, B. C. (2011) Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates. J. Anim. Ecol., 80, 1207-1216. Courtney, J. W. L., Kostelnik, M., Zeidner, N. S. and Massung, R. F. (2004) Multiplex real-time PCR for detection of Anaplasma phagocytophilum and Borrelia burgdorferi. J. Clin. Microbiol., 42, 3164-3168. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall. Birtles, R. J., Hazel, S. M., Bennett, M., Bown, K., Raoult, D. and Begon, M. (2001) Longitudinal monitoring of the dynamics of infections due to Bartonella species in UK woodland rodents. Epidem. Infectn, 126, 323-329. Robert, C. P., Rydén, G. and Titterington, D. M. (1999) Convergence controls for MCMC algorithms, with application to hidden Markov chains. J. Statist. Computn Simuln, 64, 327-355. Pradel, R. (2005) Multievent: an extension of multistate capture-recapture models to uncertain states. Biometrics, 61, 442-447. Rezek, I., Sykacek, P. and Roberts, S. J. (2000) Learning interaction dynamics with couple hidden Markov models. IEE Proc. Sci. Measmnt Technol., 147, 345-350. Guédon, Y. (2003) Estimating hidden semi-Markov chains from discrete sequences. J. Computnl Graph Statist., 12, 604-639. Chib, S. (1996) Calculating posterior distributions and modal estimates in Markov mixture models. J. Econmetr., 75, 79-97. Collett, D. (2003) Modelling Binary Data. Boca Raton: Chapman and Hall-CRC. Telfer, S., Begon, M., Bennett, M., Bown, K., Burthe, S., Lambin, X., Telford, G. and Birtles, R. (2007) Contrasting dynamics of Bartonella spp. in cyclic field vole populations: the impact of vector and host dynamics. Parasitology, 134, 413-425. Robert, C. P. and Titterington, D. M. (1998) Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. Statist. Comput., 8, 145-158. Begon, M., Telfer, S., Burthe, S. J., Lambin, X., Smith, J. M. and Paterson, S. (2009) Effects of abundance on infection in natural populations: field voles and cowpox virus. Epidemics, 1, 35-46. R Development Core Team (2012) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Fearnhead, P. and Sherlock, C. (2006) An exact Gibbs sampler for the Markov-modulated Poisson process. J. R. Statist. Soc. B, 68, 767-784. Saul, K. and Jordan, M. (1999) Mixed memory Markov models: decomposing complex stochastic processes as mixtures of simpler ones. Mach. Learn., 37, 75-87. Gelman, A. and Rubin, D. (1992) Inference from iterative simulation using multiple sequences. Statist. Sci., 7, 457-472. Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77, 257-286. Sherlock, C., Fearnhead, P. and Roberts, G. O. (2010) The random walk Metropolis: linking theory and practice through a case study. Statist. Sci., 28, 172-190. Zucchini, W. and MacDonald, I. L. (2009) Hidden Markov Models for Time Series: an Introduction using R. New York: Chapman and Hall-CRC. Roberts, G. O. and Rosenthal, J. (2001) Optimal scaling for various Metropolis-Hastings algorithms. Statist. Sci., 16, 351-367. Chadeau-Hyam, M., Clarke, P. S., Guihenneuc-Jouyaux, C., Cousens, S. N., Will, R. G. and Ghani, A. C. (2010) An application of hidden Markov models to the French variant Creutzfeldt-Jakob disease epidemic. Appl. Statist., 59, 839-853. Bennett, M., Crouch, A. J., Begon, M., Duffy, B., Feore, S., Gaskell, R. M., Kelly, D. F., McCracken, C. M., Vicary, L. and Baxby, D. (1997) Cowpox in British voles and mice. J. Compar. Path., 116, 35-44. Telfer, S., Birtles, R., Bennett, M., Lambin, X., Paterson, S. and Begon, M. (2008) Parasite interactions in natural populations: insights from longitudinal data. Parasitology, 135, 767-781. Kosoy, M., Mandel, E., Green, D., Marston, E. and Childs, J. (2004) Prospective studies of Bartonella of rodents: part I, Demographic and temporal patterns in population dynamics. Vect. Borne Zoonotic Dis., 4, 285-295. 1997; 116 2004; 42 2010; 59 2012 2002; 97 2011; 80 2004; 4 2009 1997 2008 2011; 77 1996 2007 1999; 64 2008; 74 1999; 122 2003 2005; 61 2002 2001; 126 1996; 75 2003; 12 2004; 10 1992; 7 2007; 134 1995; 82 1989; 77 1993; 16 2006; 68 2000; 147 2000; 56 2010; 28 1999; 37 2010; 330 1970; 41 2009; 162 2001; 16 2008; 135 2009; 1 2009; 15 1998; 8 Telfer (2023032107355196800_) 2010; 330 Chib (2023032107355196800_) 1996; 75 Lachish (2023032107355196800_) 2011; 80 Telfer (2023032107355196800_) 2008; 135 Courtney (2023032107355196800_) 2004; 42 Rabiner (2023032107355196800_) 1989; 77 Gelman (2023032107355196800_) 1992; 7 Robert (2023032107355196800_) 1993; 16 Sherlock (2023032107355196800_) 2010; 28 Bown (2023032107355196800_) 2004; 10 Begon (2023032107355196800_) 2009; 1 Green (2023032107355196800_) 1995; 82 Pradel (2023032107355196800_) 2005; 61 Saul (2023032107355196800_) 1999; 37 Chadeau-Hyam (2023032107355196800_) 2010; 59 Guédon (2023032107355196800_) 2003; 12 Robert (2023032107355196800_) 1998; 8 Kosoy (2023032107355196800_) 2004; 4 Roberts (2023032107355196800_) 2001; 16 Gilks (2023032107355196800_) 1996 Scott (2023032107355196800_) 2002; 97 Brand (2023032107355196800_) 1997 Natarajan (2023032107355196800_) 2007 Birtles (2023032107355196800_) 2001; 126 R Development Core Team (2023032107355196800_) 2012 Baum (2023032107355196800_) 1970; 41 Collett (2023032107355196800_) 2003 Daniels (2023032107355196800_) 2008 Bennett (2023032107355196800_) 1997; 116 Bown (2023032107355196800_) 2008; 74 Burthe (2023032107355196800_) 2009; 162 Chantrey (2023032107355196800_) 1999; 122 Bai (2023032107355196800_) 2011; 77 Robert (2023032107355196800_) 1999; 64 Zucchini (2023032107355196800_) 2009 Sherlock (2023032107355196800_) 2009; 15 Fearnhead (2023032107355196800_) 2006; 68 Rezek (2023032107355196800_) 2000; 147 Zhong (2023032107355196800_) 2002 Guihenneuc-Jouyaux (2023032107355196800_) 2000; 56 Gelman (2023032107355196800_) 1996 Telfer (2023032107355196800_) 2007; 134 Xifara (2023032107355196800_) 2012 15671735 - Vector Borne Zoonotic Dis. 2004 Winter;4(4):285-95 17096870 - Parasitology. 2007 Mar;134(Pt 3):413-25 15200860 - Emerg Infect Dis. 2004 Apr;10(4):684-7 21848864 - J Anim Ecol. 2011 Nov;80(6):1207-16 10985209 - Biometrics. 2000 Sep;56(3):733-41 18474121 - Parasitology. 2008 Jun;135(7):767-81 18820068 - Appl Environ Microbiol. 2008 Dec;74(23 ):7118-25 15243077 - J Clin Microbiol. 2004 Jul;42(7):3164-8 21352750 - Epidemics. 2009 Mar;1(1):35-46 10459650 - Epidemiol Infect. 1999 Jun;122(3):455-60 11349984 - Epidemiol Infect. 2001 Apr;126(2):323-9 9076598 - J Comp Pathol. 1997 Jan;116(1):35-44 21239553 - Appl Environ Microbiol. 2011 Mar;77(5):1728-31 16011690 - Biometrics. 2005 Jun;61(2):442-7 19916066 - Oecologia. 2010 Mar;162(3):653-61 20929776 - Science. 2010 Oct 8;330(6001):243-6
References_xml	– reference: Chantrey, J., Meyer, H., Baxby, D., Begon, M., Bown, K. J., Hazel, S. M., Jones, T., Montgomery, W. I. and Bennett, M. (1999) Cowpox: reservoir hosts and geographic range. Epidem. Infectn, 122, 455-460. – reference: Guédon, Y. (2003) Estimating hidden semi-Markov chains from discrete sequences. J. Computnl Graph Statist., 12, 604-639. – reference: Sherlock, C., Fearnhead, P. and Roberts, G. O. (2010) The random walk Metropolis: linking theory and practice through a case study. Statist. Sci., 28, 172-190. – reference: Kosoy, M., Mandel, E., Green, D., Marston, E. and Childs, J. (2004) Prospective studies of Bartonella of rodents: part I, Demographic and temporal patterns in population dynamics. Vect. Borne Zoonotic Dis., 4, 285-295. – reference: Pradel, R. (2005) Multievent: an extension of multistate capture-recapture models to uncertain states. Biometrics, 61, 442-447. – reference: Lachish, S., Knowles, S. C. L., Alves, R., Wood, M. J. and Sheldon, B. C. (2011) Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates. J. Anim. Ecol., 80, 1207-1216. – reference: Bown, K. J., Bennett, M. and Begon, M. (2004) Flea-borne Bartonella grahamii and Bartonella taylorii in Bank Voles. Emergng Infect. Dis., 10, 684-687. – reference: Telfer, S., Lambin, X., Birtles, R., Beldomenico, P., Burthe, S. J., Paterson, S. and Begon, M. (2010) Species interactions in a parasite community drive infection risk in a wildlife population. Science, 330, 243-246. – reference: Begon, M., Telfer, S., Burthe, S. J., Lambin, X., Smith, J. M. and Paterson, S. (2009) Effects of abundance on infection in natural populations: field voles and cowpox virus. Epidemics, 1, 35-46. – reference: R Development Core Team (2012) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. – reference: Chib, S. (1996) Calculating posterior distributions and modal estimates in Markov mixture models. J. Econmetr., 75, 79-97. – reference: Rezek, I., Sykacek, P. and Roberts, S. J. (2000) Learning interaction dynamics with couple hidden Markov models. IEE Proc. Sci. Measmnt Technol., 147, 345-350. – reference: Bown, K. J., Lambin, X., Telford, G. R., Ogden, N. H., Telfer, S., Woldehiwet, Z. and Birtles, R. J. (2008) Relative importance of Ixodes ricinus and Ixodes trianguliceps as vectors for Anaplasma phagocytophilum and Babesia microti in field vole (Microtus agrestis) populations. Appl. Environ. Microbiol., 74, 7118-7125. – reference: Collett, D. (2003) Modelling Binary Data. Boca Raton: Chapman and Hall-CRC. – reference: Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77, 257-286. – reference: Sherlock, C. and Roberts, G. (2009) Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets. Bernoulli, 15, 774-798. – reference: Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall. – reference: Bai, Y., Calisher, C. H., Kosoy, M. Y., Root, J. J. and Doty, J. B. (2011) Persistent infection or successive reinfection of deer mice with Bartonella vinsonii subsp. arupensis. Appl. Environ. Microbiol., 77, 1728-1731. – reference: Burthe, S. J., Lambin, X., Telfer, S., Douglas, A., Beldomenico, P., Smith, A. and Begon, M. (2009) Individual growth rates in natural field voles, Microtus agrestis, populations exhibiting cyclic population dynamics. Oecologia, 162, 653-661. – reference: Robert, C. P., Rydén, G. and Titterington, D. M. (1999) Convergence controls for MCMC algorithms, with application to hidden Markov chains. J. Statist. Computn Simuln, 64, 327-355. – reference: Guihenneuc-Jouyaux, C., Richardson, S. and Longini, I. M. (2000) Modelling markers of disease progression by a hidden Markov process: application to characterising CD4 cell decline. Biometrics, 56, 733-741. – reference: Chadeau-Hyam, M., Clarke, P. S., Guihenneuc-Jouyaux, C., Cousens, S. N., Will, R. G. and Ghani, A. C. (2010) An application of hidden Markov models to the French variant Creutzfeldt-Jakob disease epidemic. Appl. Statist., 59, 839-853. – reference: Telfer, S., Birtles, R., Bennett, M., Lambin, X., Paterson, S. and Begon, M. (2008) Parasite interactions in natural populations: insights from longitudinal data. Parasitology, 135, 767-781. – reference: Gelman, A. and Rubin, D. (1992) Inference from iterative simulation using multiple sequences. Statist. Sci., 7, 457-472. – reference: Bennett, M., Crouch, A. J., Begon, M., Duffy, B., Feore, S., Gaskell, R. M., Kelly, D. F., McCracken, C. M., Vicary, L. and Baxby, D. (1997) Cowpox in British voles and mice. J. Compar. Path., 116, 35-44. – reference: Saul, K. and Jordan, M. (1999) Mixed memory Markov models: decomposing complex stochastic processes as mixtures of simpler ones. Mach. Learn., 37, 75-87. – reference: Birtles, R. J., Hazel, S. M., Bennett, M., Bown, K., Raoult, D. and Begon, M. (2001) Longitudinal monitoring of the dynamics of infections due to Bartonella species in UK woodland rodents. Epidem. Infectn, 126, 323-329. – reference: Robert, C. P. and Titterington, D. M. (1998) Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. Statist. Comput., 8, 145-158. – reference: Baum, I. E., Petrie, Y., Soules, G. and Weiss, N. (1970) A maximisation technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist., 41, 164-171. – reference: Robert, C. P., Celeux, G. and Diebolt, J. (1993) Bayesian estimation of hidden Markov chains: a stochastic implementation. Statist. Probab. Lett., 16, 77-83. – reference: Telfer, S., Begon, M., Bennett, M., Bown, K., Burthe, S., Lambin, X., Telford, G. and Birtles, R. (2007) Contrasting dynamics of Bartonella spp. in cyclic field vole populations: the impact of vector and host dynamics. Parasitology, 134, 413-425. – reference: Fearnhead, P. and Sherlock, C. (2006) An exact Gibbs sampler for the Markov-modulated Poisson process. J. R. Statist. Soc. B, 68, 767-784. – reference: Roberts, G. O. and Rosenthal, J. (2001) Optimal scaling for various Metropolis-Hastings algorithms. Statist. Sci., 16, 351-367. – reference: Green, P. J. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732. – reference: Daniels, M. J. and Hogan, J. W. (2008) Missing Data in Longitudinal Data: Strategies for Bayesian Modelling and Sensitivity Analysis. Boca Raton: Chapman and Hall-CRC. – reference: Scott, S. L. (2002) Bayesian methods for hidden Markov models: recursive computing in the 21th century. J. Am. Statist. Ass., 97, 337-351. – reference: Courtney, J. W. L., Kostelnik, M., Zeidner, N. S. and Massung, R. F. (2004) Multiplex real-time PCR for detection of Anaplasma phagocytophilum and Borrelia burgdorferi. J. Clin. Microbiol., 42, 3164-3168. – reference: Zucchini, W. and MacDonald, I. L. (2009) Hidden Markov Models for Time Series: an Introduction using R. New York: Chapman and Hall-CRC. – volume: 134 start-page: 413 year: 2007 end-page: 425 article-title: Contrasting dynamics of Bartonella spp. in cyclic field vole populations: the impact of vector and host dynamics publication-title: Parasitology – year: 2009 – volume: 330 start-page: 243 year: 2010 end-page: 246 article-title: Species interactions in a parasite community drive infection risk in a wildlife population publication-title: Science – volume: 10 start-page: 684 year: 2004 end-page: 687 article-title: Flea‐borne and in Bank Voles publication-title: Emergng Infect. Dis. – volume: 116 start-page: 35 year: 1997 end-page: 44 article-title: Cowpox in British voles and mice publication-title: J. Compar. Path. – volume: 77 start-page: 257 year: 1989 end-page: 286 article-title: A tutorial on hidden Markov models and selected applications in speech recognition publication-title: Proc. IEEE – volume: 59 start-page: 839 year: 2010 end-page: 853 article-title: An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic publication-title: Appl. Statist. – year: 2007 – year: 2003 – year: 1996 – volume: 64 start-page: 327 year: 1999 end-page: 355 article-title: Convergence controls for MCMC algorithms, with application to hidden Markov chains publication-title: J. Statist. Computn Simuln – volume: 12 start-page: 604 year: 2003 end-page: 639 article-title: Estimating hidden semi‐Markov chains from discrete sequences publication-title: J. Computnl Graph Statist. – volume: 7 start-page: 457 year: 1992 end-page: 472 article-title: Inference from iterative simulation using multiple sequences publication-title: Statist. Sci. – volume: 135 start-page: 767 year: 2008 end-page: 781 article-title: Parasite interactions in natural populations: insights from longitudinal data publication-title: Parasitology – volume: 147 start-page: 345 year: 2000 end-page: 350 article-title: Learning interaction dynamics with couple hidden Markov models publication-title: IEE Proc. Sci. Measmnt Technol. – volume: 16 start-page: 77 year: 1993 end-page: 83 article-title: Bayesian estimation of hidden Markov chains: a stochastic implementation publication-title: Statist. Probab. Lett. – volume: 68 start-page: 767 year: 2006 end-page: 784 article-title: An exact Gibbs sampler for the Markov‐modulated Poisson process publication-title: J. R. Statist. Soc. B – volume: 82 start-page: 711 year: 1995 end-page: 732 article-title: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination publication-title: Biometrika – volume: 8 start-page: 145 year: 1998 end-page: 158 article-title: Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation publication-title: Statist. Comput. – year: 2012 – volume: 37 start-page: 75 year: 1999 end-page: 87 article-title: Mixed memory Markov models: decomposing complex stochastic processes as mixtures of simpler ones publication-title: Mach. Learn. – volume: 122 start-page: 455 year: 1999 end-page: 460 article-title: Cowpox: reservoir hosts and geographic range publication-title: Epidem. Infectn – volume: 41 start-page: 164 year: 1970 end-page: 171 article-title: A maximisation technique occurring in the statistical analysis of probabilistic functions of Markov chains publication-title: Ann. Math. Statist. – start-page: 1154 year: 2002 end-page: 1159 – volume: 75 start-page: 79 year: 1996 end-page: 97 article-title: Calculating posterior distributions and modal estimates in Markov mixture models publication-title: J. Econmetr. – volume: 97 start-page: 337 year: 2002 end-page: 351 article-title: Bayesian methods for hidden Markov models: recursive computing in the 21th century publication-title: J. Am. Statist. Ass. – volume: 162 start-page: 653 year: 2009 end-page: 661 article-title: Individual growth rates in natural field voles, , populations exhibiting cyclic population dynamics publication-title: Oecologia – volume: 61 start-page: 442 year: 2005 end-page: 447 article-title: Multievent: an extension of multistate capture‐recapture models to uncertain states publication-title: Biometrics – volume: 74 start-page: 7118 year: 2008 end-page: 7125 article-title: Relative importance of and as vectors for and in field vole ( ) populations publication-title: Appl. Environ. Microbiol. – year: 2008 – volume: 56 start-page: 733 year: 2000 end-page: 741 article-title: Modelling markers of disease progression by a hidden Markov process: application to characterising CD4 cell decline publication-title: Biometrics – volume: 16 start-page: 351 year: 2001 end-page: 367 article-title: Optimal scaling for various Metropolis‐Hastings algorithms publication-title: Statist. Sci. – year: 1997 – volume: 4 start-page: 285 year: 2004 end-page: 295 article-title: Prospective studies of of rodents: part I, Demographic and temporal patterns in population dynamics publication-title: Vect. Borne Zoonotic Dis. – volume: 28 start-page: 172 year: 2010 end-page: 190 article-title: The random walk Metropolis: linking theory and practice through a case study publication-title: Statist. Sci. – volume: 1 start-page: 35 year: 2009 end-page: 46 article-title: Effects of abundance on infection in natural populations: field voles and cowpox virus publication-title: Epidemics – volume: 80 start-page: 1207 year: 2011 end-page: 1216 article-title: Infection dynamics of endemic malaria in a wild bird population: parasite species‐dependent drivers of spatial and temporal variation in transmission rates publication-title: J. Anim. Ecol. – volume: 126 start-page: 323 year: 2001 end-page: 329 article-title: Longitudinal monitoring of the dynamics of infections due to species in UK woodland rodents publication-title: Epidem. Infectn – volume: 42 start-page: 3164 year: 2004 end-page: 3168 article-title: Multiplex real‐time PCR for detection of and publication-title: J. Clin. Microbiol. – volume: 15 start-page: 774 year: 2009 end-page: 798 article-title: Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets publication-title: Bernoulli – volume: 77 start-page: 1728 year: 2011 end-page: 1731 article-title: Persistent infection or successive reinfection of deer mice with subsp. publication-title: Appl. Environ. Microbiol. – volume: 10 start-page: 684 year: 2004 ident: 2023032107355196800_ article-title: Flea-borne Bartonella grahamii and Bartonella taylorii in Bank Voles publication-title: Emergng Infect. Dis. doi: 10.3201/eid1004.030455 – volume: 116 start-page: 35 year: 1997 ident: 2023032107355196800_ article-title: Cowpox in British voles and mice publication-title: J. Compar. Path. doi: 10.1016/S0021-9975(97)80041-2 – volume: 7 start-page: 457 year: 1992 ident: 2023032107355196800_ article-title: Inference from iterative simulation using multiple sequences publication-title: Statist. Sci. doi: 10.1214/ss/1177011136 – volume: 122 start-page: 455 year: 1999 ident: 2023032107355196800_ article-title: Cowpox: reservoir hosts and geographic range publication-title: Epidem. Infectn doi: 10.1017/S0950268899002423 – volume: 75 start-page: 79 year: 1996 ident: 2023032107355196800_ article-title: Calculating posterior distributions and modal estimates in Markov mixture models publication-title: J. Econmetr. doi: 10.1016/0304-4076(95)01770-4 – volume-title: R: a Language and Environment for Statistical Computing year: 2012 ident: 2023032107355196800_ – volume: 28 start-page: 172 year: 2010 ident: 2023032107355196800_ article-title: The random walk Metropolis: linking theory and practice through a case study publication-title: Statist. Sci. – volume: 64 start-page: 327 year: 1999 ident: 2023032107355196800_ article-title: Convergence controls for MCMC algorithms, with application to hidden Markov chains publication-title: J. Statist. Computn Simuln doi: 10.1080/00949659908811984 – volume: 97 start-page: 337 year: 2002 ident: 2023032107355196800_ article-title: Bayesian methods for hidden Markov models: recursive computing in the 21th century publication-title: J. Am. Statist. Ass. doi: 10.1198/016214502753479464 – start-page: 1154 volume-title: Proc. Int. Jt Conf. Neural Networks year: 2002 ident: 2023032107355196800_ – volume: 4 start-page: 285 year: 2004 ident: 2023032107355196800_ article-title: Prospective studies of Bartonella of rodents: part I, Demographic and temporal patterns in population dynamics publication-title: Vect. Borne Zoonotic Dis. doi: 10.1089/vbz.2004.4.285 – volume: 56 start-page: 733 year: 2000 ident: 2023032107355196800_ article-title: Modelling markers of disease progression by a hidden Markov process: application to characterising CD4 cell decline publication-title: Biometrics doi: 10.1111/j.0006-341X.2000.00733.x – volume: 77 start-page: 257 year: 1989 ident: 2023032107355196800_ article-title: A tutorial on hidden Markov models and selected applications in speech recognition publication-title: Proc. IEEE doi: 10.1109/5.18626 – volume-title: Missing Data in Longitudinal Data: Strategies for Bayesian Modelling and Sensitivity Analysis year: 2008 ident: 2023032107355196800_ doi: 10.1201/9781420011180 – volume: 330 start-page: 243 year: 2010 ident: 2023032107355196800_ article-title: Species interactions in a parasite community drive infection risk in a wildlife population publication-title: Science doi: 10.1126/science.1190333 – volume: 59 start-page: 839 year: 2010 ident: 2023032107355196800_ article-title: An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic publication-title: Appl. Statist. – volume: 16 start-page: 351 year: 2001 ident: 2023032107355196800_ article-title: Optimal scaling for various Metropolis-Hastings algorithms publication-title: Statist. Sci. doi: 10.1214/ss/1015346320 – volume-title: Coupled hidden semi Markov models for activity recognition year: 2007 ident: 2023032107355196800_ doi: 10.1109/WMVC.2007.12 – volume: 74 start-page: 7118 year: 2008 ident: 2023032107355196800_ article-title: Relative importance of Ixodes ricinus and Ixodes trianguliceps as vectors for Anaplasma phagocytophilum and Babesia microti in field vole (Microtus agrestis) populations publication-title: Appl. Environ. Microbiol. doi: 10.1128/AEM.00625-08 – volume: 68 start-page: 767 year: 2006 ident: 2023032107355196800_ article-title: An exact Gibbs sampler for the Markov-modulated Poisson process publication-title: J. R. Statist. Soc. B doi: 10.1111/j.1467-9868.2006.00566.x – volume: 37 start-page: 75 year: 1999 ident: 2023032107355196800_ article-title: Mixed memory Markov models: decomposing complex stochastic processes as mixtures of simpler ones publication-title: Mach. Learn. doi: 10.1023/A:1007649326333 – volume: 80 start-page: 1207 year: 2011 ident: 2023032107355196800_ article-title: Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates publication-title: J. Anim. Ecol. doi: 10.1111/j.1365-2656.2011.01893.x – volume-title: Modelling Binary Data year: 2003 ident: 2023032107355196800_ – volume: 126 start-page: 323 year: 2001 ident: 2023032107355196800_ article-title: Longitudinal monitoring of the dynamics of infections due to Bartonella species in UK woodland rodents publication-title: Epidem. Infectn doi: 10.1017/S095026880100526X – volume: 8 start-page: 145 year: 1998 ident: 2023032107355196800_ article-title: Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation publication-title: Statist. Comput. doi: 10.1023/A:1008938201645 – volume: 77 start-page: 1728 year: 2011 ident: 2023032107355196800_ article-title: Persistent infection or successive reinfection of deer mice with Bartonella vinsonii subsp. arupensis publication-title: Appl. Environ. Microbiol. doi: 10.1128/AEM.02203-10 – volume: 1 start-page: 35 year: 2009 ident: 2023032107355196800_ article-title: Effects of abundance on infection in natural populations: field voles and cowpox virus publication-title: Epidemics doi: 10.1016/j.epidem.2008.10.001 – volume-title: Coupled hidden Markov Models for modelling interacting processes year: 1997 ident: 2023032107355196800_ – volume: 162 start-page: 653 year: 2009 ident: 2023032107355196800_ article-title: Individual growth rates in natural field voles, Microtus agrestis, populations exhibiting cyclic population dynamics publication-title: Oecologia doi: 10.1007/s00442-009-1495-6 – volume: 61 start-page: 442 year: 2005 ident: 2023032107355196800_ article-title: Multievent: an extension of multistate capture-recapture models to uncertain states publication-title: Biometrics doi: 10.1111/j.1541-0420.2005.00318.x – volume: 134 start-page: 413 year: 2007 ident: 2023032107355196800_ article-title: Contrasting dynamics of Bartonella spp. in cyclic field vole populations: the impact of vector and host dynamics publication-title: Parasitology doi: 10.1017/S0031182006001624 – volume: 42 start-page: 3164 year: 2004 ident: 2023032107355196800_ article-title: Multiplex real-time PCR for detection of Anaplasma phagocytophilum and Borrelia burgdorferi publication-title: J. Clin. Microbiol. doi: 10.1128/JCM.42.7.3164-3168.2004 – volume: 12 start-page: 604 year: 2003 ident: 2023032107355196800_ article-title: Estimating hidden semi-Markov chains from discrete sequences publication-title: J. Computnl Graph Statist. doi: 10.1198/1061860032030 – volume: 16 start-page: 77 year: 1993 ident: 2023032107355196800_ article-title: Bayesian estimation of hidden Markov chains: a stochastic implementation publication-title: Statist. Probab. Lett. doi: 10.1016/0167-7152(93)90127-5 – volume: 135 start-page: 767 year: 2008 ident: 2023032107355196800_ article-title: Parasite interactions in natural populations: insights from longitudinal data publication-title: Parasitology doi: 10.1017/S0031182008000395 – volume-title: A hidden Markov model for disease interactions in field voles year: 2012 ident: 2023032107355196800_ – volume-title: Markov Chain Monte Carlo in Practice year: 1996 ident: 2023032107355196800_ – volume: 41 start-page: 164 year: 1970 ident: 2023032107355196800_ article-title: A maximisation technique occurring in the statistical analysis of probabilistic functions of Markov chains publication-title: Ann. Math. Statist. doi: 10.1214/aoms/1177697196 – volume-title: Markov Chain Monte Carlo in Practice year: 1996 ident: 2023032107355196800_ – volume: 147 start-page: 345 year: 2000 ident: 2023032107355196800_ article-title: Learning interaction dynamics with couple hidden Markov models publication-title: IEE Proc. Sci. Measmnt Technol. doi: 10.1049/ip-smt:20000851 – volume: 15 start-page: 774 year: 2009 ident: 2023032107355196800_ article-title: Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets publication-title: Bernoulli doi: 10.3150/08-BEJ176 – volume: 82 start-page: 711 year: 1995 ident: 2023032107355196800_ article-title: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination publication-title: Biometrika doi: 10.1093/biomet/82.4.711 – volume-title: Hidden Markov Models for Time Series: an Introduction using R year: 2009 ident: 2023032107355196800_ doi: 10.1201/9781420010893 – reference: 9076598 - J Comp Pathol. 1997 Jan;116(1):35-44 – reference: 21848864 - J Anim Ecol. 2011 Nov;80(6):1207-16 – reference: 21352750 - Epidemics. 2009 Mar;1(1):35-46 – reference: 19916066 - Oecologia. 2010 Mar;162(3):653-61 – reference: 21239553 - Appl Environ Microbiol. 2011 Mar;77(5):1728-31 – reference: 16011690 - Biometrics. 2005 Jun;61(2):442-7 – reference: 18820068 - Appl Environ Microbiol. 2008 Dec;74(23 ):7118-25 – reference: 17096870 - Parasitology. 2007 Mar;134(Pt 3):413-25 – reference: 11349984 - Epidemiol Infect. 2001 Apr;126(2):323-9 – reference: 10985209 - Biometrics. 2000 Sep;56(3):733-41 – reference: 15200860 - Emerg Infect Dis. 2004 Apr;10(4):684-7 – reference: 20929776 - Science. 2010 Oct 8;330(6001):243-6 – reference: 15671735 - Vector Borne Zoonotic Dis. 2004 Winter;4(4):285-95 – reference: 15243077 - J Clin Microbiol. 2004 Jul;42(7):3164-8 – reference: 10459650 - Epidemiol Infect. 1999 Jun;122(3):455-60 – reference: 18474121 - Parasitology. 2008 Jun;135(7):767-81
SSID	ssj0000104 ssj0018311
Score	2.1787405
Snippet	To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six... Summary To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six...
SourceID	pubmedcentral proquest pubmed crossref wiley jstor istex
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	609
SubjectTerms	Adaptive Markov chain Monte Carlo sampling Applied statistics Bartonella Covariance Datasets Discrete time Disease Disease models Diseases Economic theory Forward-backward algorithm Gibbs sampler Hidden Markov models Immune response Infections Inference Logistic regression Logistics Longitudinal studies Markov analysis Markov chains Markov models Markovian processes Mathematical models Medical research Monte Carlo simulation Original Parasites Population (statistical) Probability Public health Regression analysis Samples Sampling Statistical analysis Studies Transition probabilities Voles Zoonosis
Title	A coupled hidden Markov model for disease interactions
URI	https://api.istex.fr/ark:/67375/WNG-FTNHGBV3-F/fulltext.pdf https://www.jstor.org/stable/24771879 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frssc.12015 https://www.ncbi.nlm.nih.gov/pubmed/24223436 https://www.proquest.com/docview/1399278883 https://www.proquest.com/docview/1418136837 https://www.proquest.com/docview/1429883855 https://www.proquest.com/docview/1826583921 https://pubmed.ncbi.nlm.nih.gov/PMC3813975
Volume	62
WOSCitedRecordID	wos000321706600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library customDbUrl: eissn: 1467-9876 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0018311 issn: 0035-9254 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fa9RAEB5qz4e-WK1Wo7VEFEEhks1ubnfBl1pN-1CO0l_eW0h2N1QouXLxin--M5sf3mE5EF-OQD6Sy-xM9pvs7DcA78qCW1k6EVku40jwqoy0tCJiphCuiEtXeSWmqxM5majpVJ9uwOd-L0yrDzF8cKPI8O9rCvCibJaCfN405hPD-St9ACPGuKLGDYk4XRKPokyjW1BQnLFeoVFjUtQplVJRz58LrcxNIzLzr75M8T4C-ncd5TK_9RNUtv1_j_YYHnXENDxoPekJbLh6B7b7pg9h9w7YgS2ip62681MYH4Rmtri9cTa8Ji2SOqS9P7O70DfYCZEQh90KUEi6FPN2F0XzDC6zbxeHx1HXiSEyY-QUEfIsTBRLQYSNa2GFtYxZZI6mZNxg3DNdKV2ISnBrKyUSVyiTxNY5VgnrKr4Lm_Wsdi8grDDlTgupdVWmgtlEx9Yk2igea8M1VwF86McgN51MOXXLuMn7dIWsk3vrBPB2wN624hz3ot77oRwgaAgqZ5Np_n1ylGcXk-OjL1c8zwLY9WM9ABMhJXVjD2CvH_y8C_EmZyTpSx8QeABvhtMYnLTiUtRutkCMQALFx4rLdZhE40VUmq7BYBKYEpVlATxvfW7pTyLDE3wcgFzxxgFAAuKrZ-of115IHNka0lG870fvjWtsmJ-dnx_6o5f_An4FW4lvH0IFk3uw-XO-cK_hoblDP53v-3DFXzlV-zD6epZdnvwGVltCjw
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwED7BhsReNhgMAgOMQEhMCopjp4kfx0RXRIkmVsberMR2NKQpndp14s_nzvlBK6ZKiLdI-ZQ257v4O_v8HcDbshA2LZ0MrUijUIqqDFVqZchNIV0Rla7ySkxn4zTPs_NzddLW5tBZmEYfol9wo8jw32sKcFqQXory2XxuPnCcwJK7sClxmiE3j-XJknoUpRrtjkImOO8kGhVmRa1UKVX1_HnQyuS0SXb-1dUp3sZA_y6kXCa4foYa7vznuz2A7ZaassPGlx7CHVfvwk7X9oG1X4Fd2CKC2ug7P4LBITPTxdWls-yC1EhqRqd_pjfMt9hhSIlZuwfESJli1pyjmD-G78NPk6NR2PZiCM0AWUWITAtTxVISZRNKWmkt5xa5oym5MBj5XFWZKmQlhbVVJmNXZCaOrHO8ktZVYg826mntngKrMOlOilSpqkwkt7GKrImVyUSkjFAiC-B9NwjatELl1C_jUncJC1lHe-sE8KbHXjXyHLei3vmx7CFoCCpoSxP9Iz_Ww0k-Ov54JvQwgD0_2D0wlmlK_dgD2O9GX7dBPtecRH1pCUEE8Lq_jeFJey5F7aYLxEikUGKQiXQdJlb4kCxJ1mAwDUyIzPIAnjROt_QnkeNJMQggXXHHHkAS4qt36p8XXkoc-RoSUvzdA--Oa2yov52eHvmrZ_8CfgX3R5OvYz3-nH95DluxbyZC5ZP7sHE9W7gXcM_coM_OXvrY_Q3LyUPy
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fa9RAEB70TqQvrVZro1VXFEEhks1ubrOPtTWteITSX_YtJLsbKpTccecV_3x3Npt4h-VAfAvkIz9mZ7LfZGe_AXhXlUyLyvBQMxGFnNVVKIXmIVUlN2VUmdopMV2ORZ6nV1fyxNfm4F6YVh-i_-GGkeG-1xjgZqrrpSifzefqE7UTWHIfhhy7yAxgeHiaXYyX9KMw2fBrCimjtBNplDYv8mKlWNfz51Ir09MQLf2rq1S8i4P-XUq5THHdHJVt_efbPYJNT07JfutNj-GeabZhq2v8QPx3YBs2kKK2Cs9PYLRP1GQxvTGaXKMeSUNw_8_klrgmO8SSYuJXgQhqU8zanRTzp3CRfTk_OA59N4ZQjSyvCC3XsslixZG0Mck115pSbdmjqihTNvaprFNZ8pozreuUx6ZMVRxpY2jNtanZDgyaSWN2gdQ27U5KIWVdJZzqWEZaxVKlLJKKSZYG8KEbhEJ5qXLsmHFTdCkLWqdw1gngbY-dtgIdd6Leu7HsIdYQWNImkuJ7flRk5_nx0edLVmQB7LjB7oExFwI7sgew141-4cN8XlCU9cWfCCyAN_1pG6C46lI2ZrKwGG5JFBulTKzDxNJeJE2SNRibCCZIZ2kAz1qnW3pIy_I4GwUgVtyxB6CI-OqZ5se1ExO3jM1SUnvfj84d19iwOD07O3BHz_8F_Boenhxmxfhr_u0FbMSumwjWT-7B4OdsYV7CA3VrXXb2ygfvb9fPRQg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Coupled+Hidden+Markov+Model+for+Disease+Interactions&rft.jtitle=Journal+of+the+Royal+Statistical+Society+Series+C%3A+Applied+Statistics&rft.au=Sherlock%2C+Chris&rft.au=Xifara%2C+Tatiana&rft.au=Telfer%2C+Sandra&rft.au=Begon%2C+Mike&rft.date=2013-08-01&rft.issn=0035-9254&rft.eissn=1467-9876&rft.volume=62&rft.issue=4&rft.spage=609&rft.epage=627&rft_id=info:doi/10.1111%2Frssc.12015&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_rssc_12015
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0035-9254&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0035-9254&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0035-9254&client=summon