Epidemic detection enables swift public health responses. Dengue viruses pose a significant public health challenge in Puerto Rico, where they are endemic and cause intermittent epidemics. A weekly intercept-only negative binomial regression model was fitted using historical data from January 1986 to June 2024. Thresholds were defined using three percentiles (60%, 75%, and 90%). The 75th percentile threshold aligned best with historical epidemic classifications. This model provides a robust method for defining thresholds, accounting for skewed data, utilizing all historical data, and improving upon traditional methods like endemic channels. In March 2024, the Puerto Rico Department of Health declared a public health emergency due to an unseasonably early surge in cases that exceeded the epidemic alert threshold in February. This real-time application highlights the value of these thresholds to support dengue epidemic detection and public health response. Integrating thresholds with other tools and strategies can enhance epidemic preparedness and management.

The authors have declared no competing interest.

This study did not receive any funding.

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Historical dengue surveillance data from Puerto Rico's Passive Arboviral Disease Surveillance System: https://forms.office.com/pages/responsepage.aspx?id=WgYG6T7wrUekxGsTmghEXBHzWo_iDtJOjtyx8LYE1wtUQTY3VjBIVVU3M1pSWDNJMUJGQk8zWDNNViQlQCN0PWcu&origin=QRCode&route=shorturl%3E

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