Quantifying, Understanding, and Correcting for Delays in Routine Dengue Case Reporting in the Americas

Dengue virus is a growing public health challenge in the Americas, where endemic-epidemic transmission and unprecedented surges demand timely and accurate surveillance. Although real-time reporting, such as the Pan American Health Organization's (PAHO) database, is central to trend analysis, its case counts are subject to reporting delays and backfilling, which can lead to an initial underestimation of the size of an epidemic and result in insufficient control efforts. A novel, longitudinal dataset of weekly WHO PAHO dengue reports (2022-2025) was compiled, capturing successive data across 46 countries and territories. This dataset enables quantification of reporting factors across delays of up to 60 weeks. Analysis reveals that 91.4% of countries exhibit reporting delays. The average threshold to reach the final case count was 13.9 weeks (interquartile range: 3-17 weeks). Regression modeling revealed that countries that were classified as at very low risk for disaster and had a small population experienced fewer delays in reporting. When the size and severity of an outbreak are considered, severity exerts a stronger influence, resulting in fewer timely reports. The marked heterogeneity across countries in delay dynamics underscores the need to understand and correct for the causes of reporting delays on a country-by-country basis. The study findings provide methodological insights into incorporating robust priors into near-real-time forecasting when direct observations of reporting delay are unavailable. A foundation for improved modeling, enhanced outbreak response, and more resilient public health decision-making at both regional and global scales is established in the present study.