ArticleStatistics in medicine2019
A modelling approach for correcting reporting delays in disease surveillance data.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparative effectiveness of non-pharmacological interventions for depression and anxiety in chronic low back pain: a Bayesian network meta-analysis of randomized controlled trials.Frontiers in public health · 2026Pooled it
- Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.Infectious diseases of poverty · 2023Pooled it
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- Quantifying, Understanding, and Correcting for Delays in Routine Dengue Case Reporting in the Americas.The American journal of tropical medicine and hygiene · 2026Article
- Global patterns of pertussis incidence and vaccination strategies: implications of divergent surveillance data.NPJ vaccines · 2026Article
- Joint Bayesian Nowcasting of Severe Acute Respiratory Illness and COVID-19 Positives in Brazil.Statistics in medicine · 2026Article
- Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.Scientific data · 2026Article
- Article
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- A statistical model for forecasting probabilistic epidemic bands for dengue cases in Brazil.Infectious Disease Modelling · 2025Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- Primary health care data-based early warning system for dengue outbreaks: a nationwide case study in Brazil.Lancet regional health. Americas · 2025Article
- Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.PLoS neglected tropical diseases · 2025Article
- Real-time monitoring of excess mortality under a new endemic regime.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Accounting for reporting delays in real-time phylodynamic analyses with preferential sampling.PLoS computational biology · 2025Article
- A renewal-equation approach to estimatingPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Post-processing and weighted combination of infectious disease nowcasts.PLoS computational biology · 2025Article
- Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data.PLoS computational biology · 2025Article
- Public health actions in response to pathogen detection in wastewater and the environment: a scoping review.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.