Optimizing Dengue Surveillance Thresholds in Malaysia: A Comparative Evaluation of Endemic Channel Approaches
Tropical Medicine and Infectious Disease·
- DOI
- 10.3390/tropicalmed11080231
- PMID
- —
- PMCID
- —
- OpenAlex
- W7203777959
- Study type
- Journal article
- Publisher
- MDPI AG
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Routine application of the optimized thresholding framework could assist national monitoring programs in balancing alert frequency with detection reliability.
Structured evidence summary
Research question
The study evaluates whether a log-scale statistical thresholding method with an enhanced alert rule outperforms traditional fixed standard deviation approaches for detecting dengue outbreaks in Malaysia.
Study design
Researchers conducted a retrospective comparative analysis using national surveillance records, applying a rolling validation framework across multiple three-year baseline periods before testing the selected model on recent operational data.
Population and setting
The analysis utilized aggregated weekly dengue case counts reported nationwide in Malaysia over an eleven-year period.
Main findings
The modified log-scale thresholding strategy demonstrated consistently higher diagnostic accuracy, sensitivity, and specificity compared to the conventional method during validation windows, establishing it as the more reliable detection framework.
Public-health relevance
Routine application of the optimized thresholding framework could assist national monitoring programs in balancing alert frequency with detection reliability.
Important limitations
The provided materials contain no explicit methodological constraints or bias assessments. Consequently, this overview depends entirely on the supplied abstract and metadata, necessitating access to the complete manuscript for rigorous appraisal.
GIDS interpretation
This record offers methodological reference points for statistical threshold calibration in arbovirus monitoring systems. It serves as background literature for analytical planning rather than an indicator of current epidemiological activity.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 8 auditable classifier relationships to diseases, places, topics, and study design.