Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2024

Open Access 01-12-2024 | Research

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand

Authors: Chawarat Rotejanaprasert, Darin Areechokchai, Richard J. Maude

Published in: BMC Medical Research Methodology | Issue 1/2024

Login to get access

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.
Appendix
Available only for authorised users
Literature
1.
2.
3.
Thawillarp S, Castillo-Salgado C, Lehmann HP. Evaluation of early aberration reporting system for Dengue Outbreak Detection in Thailand. OSIR J. 2018;11(4):1–6.CrossRef
4.
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):786–91.CrossRefPubMed
5.
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):653–60.CrossRefPubMedPubMedCentral
6.
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):4363–77.CrossRefPubMedPubMedCentral
7.
Lin H, Yip PS, Huggins RM. A double-nonparametric procedure for estimating the number of delay‐reported cases. Stat Med. 2008;27(17):3325–39.CrossRefPubMed
8.
Stoner O, Economou T. Multivariate hierarchical frameworks for modeling delayed reporting in count data. Biometrics. 2019.
9.
Salmon M, Schumacher D, Stark K, Höhle M. Bayesian outbreak detection in the presence of reporting delays. Biom J. 2015;57(6):1051–67.CrossRefPubMed
10.
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.CrossRefPubMedPubMedCentral
11.
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):1–13.CrossRef
12.
Osterholm MT, Hedberg CW. 13 - epidemiologic principles. In: Bennett JE, Dolin R, Blaser MJ, editors. Mandell, Douglas, and Bennett’s principles and practice of Infectious diseases (Eighth Edition). Philadelphia: W.B. Saunders; 2015. 146 – 57.e2.
13.
Rotejanaprasert C, Lawson A. Bayesian prospective detection of small area health anomalies using kullback–leibler divergence. Stat Methods Med Res. 2018;27(4):1076–87.CrossRefPubMed
14.
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.CrossRefPubMedPubMedCentral
15.
Lawson AB, Banerjee S, Haining RP, Ugarte MD. Handbook of spatial epidemiology. CRC Press; 2016.
16.
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.
17.
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.CrossRefPubMedPubMedCentral
18.
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):1–11.CrossRef
19.
Besag J, York J, Mollié A. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math. 1991;43(1):1–20.CrossRef
20.
Ver Hoef JM, Boveng PL. Quasi-poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology. 2007;88(11):2766–72.CrossRefPubMed
21.
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):219–29.CrossRef
22.
Harris T, Yang Z, Hardin JW. Modeling underdispersed count data with generalized Poisson regression. Stata J. 2012;12(4):736–47.CrossRef
23.
Consul PC, Jain GC. A generalization of the Poisson distribution. Technometrics. 1973;15(4):791–9.CrossRef
24.
Zamani H, Ismail N. Functional form for the generalized Poisson regression model. Commun Statistics-Theory Methods. 2012;41(20):3666–75.CrossRef
25.
Besag J, Newell J. The detection of clusters in rare diseases. J Royal Stat Society: Ser (Statistics Society). 1991;154(1):143–55.CrossRef
26.
Kulldorff M. A spatial scan statistic. Commun Statistics-Theory Methods. 1997;26(6):1481–96.CrossRef
27.
Kulldorff M. Spatial scan statistics: models, calculations, and applications. Scan statistics and applications: Springer; 1999. pp. 303–22.CrossRef
28.
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.
29.
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.CrossRef
30.
Lawson AB. Disease Cluster detection: a critique and a bayesian proposal. Stat Med. 2006;25(5):897–916.CrossRefPubMed
31.
Lawson AB, Rotejanaprasert C. Childhood brain cancer in Florida: a bayesian clustering approach. Stat Public Policy. 2014;1(1):99–107.CrossRef
32.
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):583–639.CrossRef
33.
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).
34.
Vehtari A, Gelman A, Gabry J. Practical bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):1413–32.CrossRef
35.
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):1101–17.CrossRefPubMedPubMedCentral
36.
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):1657–71.CrossRefPubMedPubMedCentral
37.
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):250–68.CrossRefPubMed
38.
Pettit L. The conditional predictive ordinate for the normal distribution. J Roy Stat Soc: Ser B (Methodol). 1990;52(1):175–84.
39.
Dawid AP. Present position and potential developments: some personal views statistical theory the prequential approach. J Royal Stat Society: Ser (General). 1984;147(2):278–90.CrossRef
40.
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):1–22.CrossRef
41.
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):319–92.CrossRef
Metadata
Title
Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand
Authors
Chawarat Rotejanaprasert
Darin Areechokchai
Richard J. Maude
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2024
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-024-02141-5

Other articles of this Issue 1/2024

BMC Medical Research Methodology 1/2024 Go to the issue

Research

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

Research

Fidelity, pragmatism and the “grey line” in between—exploring the delivery of a pragmatic physical activity randomised controlled trial—a secondary analysis

Research

Revising model for end-stage liver disease from calendar-time cross-sections with correction for selection bias

Research

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

Research article

A flexible approach to measure care coordination based on patient-sharing networks

Research

A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study