Dengue epidemic alert thresholds

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Overview

This repository supports the manuscript "Dengue epidemic alert thresholds: a tool for surveillance and epidemic detection" (preprint posted October 28, 2024). It provides code and supporting documentation for deriving weekly dengue epidemic thresholds using historical case data and a statistical modeling framework. The approach informs real-time public health decision-making.

Background

Dengue fever remains a critical public health concern in Puerto Rico and other endemic regions. Our method uses a weekly intercept-only negative binomial regression model, fit to decades of historical data, to estimate threshold values (60th, 75th, and 90th percentiles). These thresholds enable objective, data-driven identification of epidemic weeks and timely alerts.

Repository Contents

• /data/weekly_data_dengue_epidemic_alert_thresholds_1986_June2024.csv – (.csv file) De-identified, cleaned historical weekly dengue case data, Puerto Rico 1986-June 2024.
• /code/threshold_model.R - (.R file) Primary R script for running thresholds and plotting figures
  • Fits the intercept-only negative binomial model using data from January 1986 to June 2024.
  • Calculates percentile-based thresholds (60%, 75%, 90%).
  • Generates graphs.
• /.github/ - (.md files) issue and pull request templates

Usage Instructions

1. Download data: Either 1) use example data currently in /data folder (weekly_data_dengue_epidemic_alert_thresholds_1986_June2024.csv) or 2) place your own de-identified, week-level dengue case data in the /data folder, as an R-readable file (e.g., CSV, RDS).
2. Run the model: Execute threshold_model.R. This will compute thresholds and generate key figures.

These examples demonstrate practical impact and assist in bridging surveillance data with actionable public health responses.

Citation

If you use these materials or replicate the modeling approach, please cite: Thayer MB, Marzan-Rodriguez M, Torres Aponte J, et al. Dengue epidemic alert thresholds: a tool for surveillance and epidemic detection. medRxiv (preprint). 2024. DOI:10.1101/2024.10.22.24315684.

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