ArticleInternational journal of health geographics2020
Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.
Article in International journal of health geographics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.Journal of the Royal Society, Interface · 2021Pooled it
- Dengue epidemiology and transmission intensity across Panama during 2000-2024: a modelling study.Lancet regional health. Americas · 2026Article
- Modeling the impact of anthrax vaccination on buffalo outbreak dynamics in northern Vietnam.One health (Amsterdam, Netherlands) · 2026Article
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.Infectious Disease Modelling · 2026Article
- Spatiotemporal epidemiology and associated risk factors of tuberculosis incidence and mortality in Indonesia 2017-2022: a nationwide space-time hierarchical analysis.Population health metrics · 2026Article
- Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.PLoS neglected tropical diseases · 2025Article
- Spatial occurrence-intensity modeling of dengue incidence in southernmost provinces of Thailand.PLoS neglected tropical diseases · 2025Article
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- Nowcasting to Monitor Real-Time Mpox Trends During the 2022 Outbreak in New York City: Evaluation Using Reportable Disease Data Stratified by Race or Ethnicity.Online journal of public health informatics · 2025Article
- Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand.Scientific reports · 2024Article
- Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection.Malaria journal · 2024Article
- Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study.PLoS neglected tropical diseases · 2024Article
- Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.Viruses · 2024Article
- Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.BMC medical research methodology · 2024Article
- Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand.BMC medical research methodology · 2024Article
- Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.BMC medical research methodology · 2023Article
- Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models.Scientific reports · 2023Article
- Article
- Faster indicators of chikungunya incidence using Google searches.PLoS neglected tropical diseases · 2022Article
- Timely epidemic monitoring in the presence of reporting delays: anticipating the COVID-19 surge in New York City, September 2020.BMC public health · 2022Article
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Abstract
backgroundThe ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.
methodsPredicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010.
resultsThe overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data.
conclusionsA persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.
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