{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:12:06Z","timestamp":1783127526526,"version":"3.54.6"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004156","name":"Mahidol University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004156","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Res Methodol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12874-024-02141-5","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T18:01:34Z","timestamp":1705168894000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand"],"prefix":"10.1186","volume":"24","author":[{"given":"Chawarat","family":"Rotejanaprasert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darin","family":"Areechokchai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard J.","family":"Maude","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"issue":"7446","key":"2141_CR1","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1038\/nature12060","volume":"496","author":"S Bhatt","year":"2013","unstructured":"Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496(7446):504\u20137.","journal-title":"Nature"},{"issue":"5","key":"2141_CR2","doi-asserted-by":"publisher","first-page":"743","DOI":"10.4269\/ajtmh.2012.12-0157","volume":"86","author":"DJ Gubler","year":"2012","unstructured":"Gubler DJ. The economic burden of dengue. Am J Trop Med Hyg. 2012;86(5):743.","journal-title":"Am J Trop Med Hyg"},{"issue":"4","key":"2141_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59096\/osir.v11i4.263047","volume":"11","author":"S Thawillarp","year":"2018","unstructured":"Thawillarp S, Castillo-Salgado C, Lehmann HP. Evaluation of early aberration reporting system for Dengue Outbreak Detection in Thailand. OSIR J. 2018;11(4):1\u20136.","journal-title":"OSIR J"},{"issue":"6","key":"2141_CR4","doi-asserted-by":"publisher","first-page":"786","DOI":"10.4269\/ajtmh.2005.72.786","volume":"72","author":"DV Clark","year":"2005","unstructured":"Clark DV, Mammen MP Jr, Nisalak A, Puthimethee V, Endy TP. Economic impact of dengue fever\/dengue hemorrhagic fever in Thailand at the family and population levels. Am J Trop Med Hyg. 2005;72(6):786\u201391.","journal-title":"Am J Trop Med Hyg"},{"issue":"11","key":"2141_CR5","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1093\/trstmh\/trz068","volume":"113","author":"N Raafat","year":"2019","unstructured":"Raafat N, Blacksell SD, Maude RJ. A review of dengue diagnostics and implications for surveillance and control. Trans R Soc Trop Med Hyg. 2019;113(11):653\u201360.","journal-title":"Trans R Soc Trop Med Hyg"},{"issue":"22","key":"2141_CR6","doi-asserted-by":"publisher","first-page":"4363","DOI":"10.1002\/sim.8303","volume":"38","author":"LS Bastos","year":"2019","unstructured":"Bastos LS, Economou T, Gomes MFC, Villela DAM, Coelho FC, Cruz OG, et al. A modelling approach for correcting reporting delays in disease surveillance data. Stat Med. 2019;38(22):4363\u201377.","journal-title":"Stat Med"},{"issue":"17","key":"2141_CR7","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1002\/sim.3183","volume":"27","author":"H Lin","year":"2008","unstructured":"Lin H, Yip PS, Huggins RM. A double-nonparametric procedure for estimating the number of delay\u2010reported cases. Stat Med. 2008;27(17):3325\u201339.","journal-title":"Stat Med"},{"key":"2141_CR8","doi-asserted-by":"crossref","unstructured":"Stoner O, Economou T. Multivariate hierarchical frameworks for modeling delayed reporting in count data. Biometrics. 2019.","DOI":"10.1111\/biom.13188"},{"issue":"6","key":"2141_CR9","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1002\/bimj.201400159","volume":"57","author":"M Salmon","year":"2015","unstructured":"Salmon M, Schumacher D, Stark K, H\u00f6hle M. Bayesian outbreak detection in the presence of reporting delays. Biom J. 2015;57(6):1051\u201367.","journal-title":"Biom J"},{"issue":"4","key":"2141_CR10","doi-asserted-by":"publisher","first-page":"e1007735","DOI":"10.1371\/journal.pcbi.1007735","volume":"16","author":"SF McGough","year":"2020","unstructured":"McGough SF, Johansson MA, Lipsitch M, Menzies NA. Nowcasting by bayesian smoothing: a flexible, generalizable model for real-time epidemic tracking. PLoS Comput Biol. 2020;16(4):e1007735.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2141_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12942-020-00199-0","volume":"19","author":"C Rotejanaprasert","year":"2020","unstructured":"Rotejanaprasert C, Ekapirat N, Areechokchai D, Maude RJ. Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand. Int J Health Geogr. 2020;19(1):1\u201313.","journal-title":"Int J Health Geogr"},{"key":"2141_CR12","volume-title":"Mandell, Douglas, and Bennett\u2019s principles and practice of Infectious diseases (Eighth Edition)","author":"MT Osterholm","year":"2015","unstructured":"Osterholm MT, Hedberg CW. 13 - epidemiologic principles. In: Bennett JE, Dolin R, Blaser MJ, editors. Mandell, Douglas, and Bennett\u2019s principles and practice of Infectious diseases (Eighth Edition). Philadelphia: W.B. Saunders; 2015. 146\u2009\u2013\u200957.e2."},{"issue":"4","key":"2141_CR13","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1177\/0962280216652156","volume":"27","author":"C Rotejanaprasert","year":"2018","unstructured":"Rotejanaprasert C, Lawson A. Bayesian prospective detection of small area health anomalies using kullback\u2013leibler divergence. Stat Methods Med Res. 2018;27(4):1076\u201387.","journal-title":"Stat Methods Med Res"},{"issue":"9","key":"2141_CR14","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/ijerph15092042","volume":"15","author":"C Rotejanaprasert","year":"2018","unstructured":"Rotejanaprasert C, Lawson AB. A bayesian quantile modeling for spatiotemporal relative risk: an application to adverse risk detection of respiratory diseases in South Carolina, USA. Int J Environ Res Public Health. 2018;15(9):2042.","journal-title":"Int J Environ Res Public Health"},{"key":"2141_CR15","doi-asserted-by":"crossref","unstructured":"Lawson AB, Banerjee S, Haining RP, Ugarte MD. Handbook of spatial epidemiology. CRC Press; 2016.","DOI":"10.1201\/b19470"},{"key":"2141_CR16","doi-asserted-by":"crossref","unstructured":"Aswi A, Cramb S, Moraga P, Mengersen K. Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review. Epidemiol Infect. 2019;147.","DOI":"10.1017\/S0950268818002807"},{"issue":"9","key":"2141_CR17","doi-asserted-by":"publisher","first-page":"e0239645","DOI":"10.1371\/journal.pone.0239645","volume":"15","author":"C Rotejanaprasert","year":"2020","unstructured":"Rotejanaprasert C, Lawpoolsri S, Pan-Ngum W, Maude RJ. Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand. PLoS ONE. 2020;15(9):e0239645.","journal-title":"PLoS ONE"},{"issue":"1","key":"2141_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-019-0833-6","volume":"19","author":"C Rotejanaprasert","year":"2019","unstructured":"Rotejanaprasert C, Lawson AB, Iamsirithaworn S. Spatiotemporal multi-disease transmission dynamic measure for emerging diseases: an application to dengue and zika integrated surveillance in Thailand. BMC Med Res Methodol. 2019;19(1):1\u201311.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"2141_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF00116466","volume":"43","author":"J Besag","year":"1991","unstructured":"Besag J, York J, Molli\u00e9 A. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math. 1991;43(1):1\u201320.","journal-title":"Ann Inst Stat Math"},{"issue":"11","key":"2141_CR20","doi-asserted-by":"publisher","first-page":"2766","DOI":"10.1890\/07-0043.1","volume":"88","author":"JM Ver Hoef","year":"2007","unstructured":"Ver Hoef JM, Boveng PL. Quasi-poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology. 2007;88(11):2766\u201372.","journal-title":"Ecology"},{"issue":"2","key":"2141_CR21","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1002\/bimj.200410102","volume":"47","author":"H Joe","year":"2005","unstructured":"Joe H, Zhu R. Generalized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences. 2005;47(2):219\u201329.","journal-title":"Biometrical Journal: Journal of Mathematical Methods in Biosciences"},{"issue":"4","key":"2141_CR22","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1177\/1536867X1201200412","volume":"12","author":"T Harris","year":"2012","unstructured":"Harris T, Yang Z, Hardin JW. Modeling underdispersed count data with generalized Poisson regression. Stata J. 2012;12(4):736\u201347.","journal-title":"Stata J"},{"issue":"4","key":"2141_CR23","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1080\/00401706.1973.10489112","volume":"15","author":"PC Consul","year":"1973","unstructured":"Consul PC, Jain GC. A generalization of the Poisson distribution. Technometrics. 1973;15(4):791\u20139.","journal-title":"Technometrics"},{"issue":"20","key":"2141_CR24","doi-asserted-by":"publisher","first-page":"3666","DOI":"10.1080\/03610926.2011.564742","volume":"41","author":"H Zamani","year":"2012","unstructured":"Zamani H, Ismail N. Functional form for the generalized Poisson regression model. Commun Statistics-Theory Methods. 2012;41(20):3666\u201375.","journal-title":"Commun Statistics-Theory Methods"},{"issue":"1","key":"2141_CR25","doi-asserted-by":"publisher","first-page":"143","DOI":"10.2307\/2982708","volume":"154","author":"J Besag","year":"1991","unstructured":"Besag J, Newell J. The detection of clusters in rare diseases. J Royal Stat Society: Ser (Statistics Society). 1991;154(1):143\u201355.","journal-title":"J Royal Stat Society: Ser (Statistics Society)"},{"issue":"6","key":"2141_CR26","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1080\/03610929708831995","volume":"26","author":"M Kulldorff","year":"1997","unstructured":"Kulldorff M. A spatial scan statistic. Commun Statistics-Theory Methods. 1997;26(6):1481\u201396.","journal-title":"Commun Statistics-Theory Methods"},{"key":"2141_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-1-4612-1578-3_14","volume-title":"Spatial scan statistics: models, calculations, and applications","author":"M Kulldorff","year":"1999","unstructured":"Kulldorff M. Spatial scan statistics: models, calculations, and applications. Scan statistics and applications: Springer; 1999. pp. 303\u201322."},{"key":"2141_CR28","unstructured":"Kulldorff M. SaTScan (TM) v7. 0: Software for the spatial and space-time scan statistics. Information Management Services, Inc) Available at http:\/\/satscan org [Verified 5 October 2009]. 2006."},{"issue":"3","key":"2141_CR29","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1097\/EDE.0000000000000450","volume":"27","author":"A Kim","year":"2016","unstructured":"Kim A, Wakefield J. A bayesian method for cluster detection with application to five cancer sites in Puget Sound. Epidemiol (Cambridge Mass). 2016;27(3):347.","journal-title":"Epidemiol (Cambridge Mass)"},{"issue":"5","key":"2141_CR30","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1002\/sim.2417","volume":"25","author":"AB Lawson","year":"2006","unstructured":"Lawson AB. Disease Cluster detection: a critique and a bayesian proposal. Stat Med. 2006;25(5):897\u2013916.","journal-title":"Stat Med"},{"issue":"1","key":"2141_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1080\/2330443X.2014.970247","volume":"1","author":"AB Lawson","year":"2014","unstructured":"Lawson AB, Rotejanaprasert C. Childhood brain cancer in Florida: a bayesian clustering approach. Stat Public Policy. 2014;1(1):99\u2013107.","journal-title":"Stat Public Policy"},{"issue":"4","key":"2141_CR32","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1111\/1467-9868.00353","volume":"64","author":"DJ Spiegelhalter","year":"2002","unstructured":"Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J Royal Stat Society: Ser b (Statistical Methodology). 2002;64(4):583\u2013639.","journal-title":"J Royal Stat Society: Ser b (Statistical Methodology)"},{"key":"2141_CR33","unstructured":"Watanabe S, Opper M. Asymptotic equivalence of Bayes Cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11(12)."},{"issue":"5","key":"2141_CR34","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","volume":"27","author":"A Vehtari","year":"2017","unstructured":"Vehtari A, Gelman A, Gabry J. Practical bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):1413\u201332.","journal-title":"Stat Comput"},{"issue":"4","key":"2141_CR35","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1177\/0962280216660422","volume":"25","author":"C Rotejanaprasert","year":"2016","unstructured":"Rotejanaprasert C, Lawson A, Bolick-Aldrich S, Hurley D. Spatial bayesian surveillance for small area case event data. Stat Methods Med Res. 2016;25(4):1101\u201317.","journal-title":"Stat Methods Med Res"},{"issue":"6","key":"2141_CR36","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1016\/j.csda.2010.01.025","volume":"54","author":"DC Wheeler","year":"2010","unstructured":"Wheeler DC, Hickson DA, Waller LA. Assessing local model adequacy in bayesian hierarchical models using the partitioned deviance information criterion. Comput Stat Data Anal. 2010;54(6):1657\u201371.","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"2141_CR37","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1177\/0962280215627298","volume":"27","author":"R Carroll","year":"2018","unstructured":"Carroll R, Lawson AB, Faes C, Kirby RS, Aregay M, Watjou K. Spatially-dependent bayesian model selection for disease mapping. Stat Methods Med Res. 2018;27(1):250\u201368.","journal-title":"Stat Methods Med Res"},{"issue":"1","key":"2141_CR38","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1111\/j.2517-6161.1990.tb01780.x","volume":"52","author":"L Pettit","year":"1990","unstructured":"Pettit L. The conditional predictive ordinate for the normal distribution. J Roy Stat Soc: Ser B (Methodol). 1990;52(1):175\u201384.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"2","key":"2141_CR39","doi-asserted-by":"publisher","first-page":"278","DOI":"10.2307\/2981683","volume":"147","author":"AP Dawid","year":"1984","unstructured":"Dawid AP. Present position and potential developments: some personal views statistical theory the prequential approach. J Royal Stat Society: Ser (General). 1984;147(2):278\u201390.","journal-title":"J Royal Stat Society: Ser (General)"},{"issue":"1","key":"2141_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2288-12-137","volume":"12","author":"C Adrion","year":"2012","unstructured":"Adrion C, Mansmann U. Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint. BMC Med Res Methodol. 2012;12(1):1\u201322.","journal-title":"BMC Med Res Methodol"},{"issue":"2","key":"2141_CR41","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1111\/j.1467-9868.2008.00700.x","volume":"71","author":"H Rue","year":"2009","unstructured":"Rue H, Martino S, Chopin N. Approximate bayesian inference for latent gaussian models by using integrated nested Laplace approximations. J Royal Stat Society: Ser b (Statistical Methodology). 2009;71(2):319\u201392.","journal-title":"J Royal Stat Society: Ser b (Statistical Methodology)"}],"container-title":["BMC Medical Research Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-024-02141-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12874-024-02141-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-024-02141-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:47:09Z","timestamp":1731026829000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedresmethodol.biomedcentral.com\/articles\/10.1186\/s12874-024-02141-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2141"],"URL":"https:\/\/doi.org\/10.1186\/s12874-024-02141-5","relation":{},"ISSN":["1471-2288"],"issn-type":[{"value":"1471-2288","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"10 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethics Committee of the Faculty of Tropical Medicine, Mahidol University waived for informed consent of participants. This study was approved by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University. The submission number was TMEC 22\u2013054 and the number of ethical approval certificate was MUTM 2022-057-01. All methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}