Lessons learned from real-time nowcasting: The 2024 dengue outbreak in Puerto Rico

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Abstract Real-time nowcasting enhances situational awareness by mitigating reporting delays that obscure transmission dynamics. We applied Nowcasting by Bayesian Smoothing (NobBS) to the 2024 dengue outbreak in Puerto Rico (PR), using case surveillance data from the PR Department of Health. The method accurately captured the epidemic trajectory and consistently outperformed a baseline model, although reporting anomalies occasionally reduced performance. We also conducted analyses by dengue virus serotype and health region, as well as previous years. For analyses with few dengue cases, a model in which parameters are jointly estimated across groups generally achieved better performance than the independent one. Historical analyses revealed that years with higher variability in reporting delays generally exhibited higher uncertainty. The findings here underscore key lessons for real-time dengue nowcasting: alternative models may be needed in complex circumstances, but with stable reporting patterns and continuous evaluation, nowcasts can be a reliable and valuable public health tool.

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