The Puerto Rico Department of Health (PRDH) seeks to identify dengue epidemics as early as possible with high specificity.

Development and prospective application of an early warning system for dengue epidemics using routine historical surveillance data. A weekly intercept-only negative binomial regression model was fitted using historical probable and confirmed dengue data. A range of threshold definitions was explored using three model-estimated percentiles of weekly dengue case counts.

Dengue is endemic in Puerto Rico with irregular occurrence of large epidemics with substantial impact on health burden and health systems. Probable and confirmed dengue data are routinely collected from all hospitals and private clinics.

A total of 86 282 confirmed or probable dengue virus cases were reported from 1 January 1986 to 30 June 2024, with an annual mean of 2212 cases (median: 1533; range: 40–10 356).

The model was fitted retrospectively to mimic real-time epidemic detection and assessed based on sensitivity and specificity of epidemic detection.

The 75th percentile threshold aligned best with historical epidemic classifications, balancing false alarms and missed detections. This model provides a robust method for defining thresholds, accounting for skewed data, using all historical data and improving on traditional methods like endemic channels.

In March 2024, PRDH declared a public health emergency due to an early, out-of-season surge in cases that exceeded the epidemic alert threshold developed in this study. 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.

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer mortality worldwide. Despite the organised CRC screening programme, the uptake rate of the population-based CRC screening was still low. Thus, we will conduct a randomised controlled trial in a community setting to evaluate the effectiveness of a theory-based chatbot in promoting CRC screening uptake.

A total of 500 eligible participants will be randomly assigned to a WhatsApp Messenger-initiated chatbot outreach group or a standard text reminder group at a ratio of 1:1. The intervention group will deliver Chinese culturally tailored education texts and videos developed based on the Health Belief Model and the Trans-Theoretical Model. The control group will deliver a standard text reminder of information about the Hong Kong organised CRC screening programme. In addition to the baseline assessment and postintervention assessment, all subjects will be followed up for 3 months and 6 months, respectively. The primary outcome will be the CRC screening uptake rate at the 3 month and 6 month follow-up. The secondary outcomes will be the intention to undergo CRC screening uptake, time interval to participate in and complete screening after recruitment, and reasons for not participating in screening at the 3 month and 6 month follow-up. Quantitative data will be analysed using Student’s t-test, Pearson’s ² test or Fisher’s exact test. Qualitative data will be analysed by thematic analysis.

Ethical approval of this trial was granted by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (2022.614). Written informed consent will be obtained from study participants before enrolment. The findings will be disseminated through peer-reviewed journals.

The study was registered on clinicaltrials.gov (NCT06192862).