A two-parameter, minimal-data model to predict dengue cases: the 2022-2023 outbreak in Florida, USA

TL;DR

This paper introduces a simple, two-parameter dengue outbreak prediction model that effectively forecasts cases using minimal data, suitable for resource-limited settings, demonstrated on Florida's 2022-2023 outbreak.

Contribution

It extends the ICC curve-based model to a two-population SEIR framework and incorporates Bayesian uncertainty quantification, enabling accurate predictions with minimal data.

Findings

Competitive predictive performance in Florida's outbreak

Lower computational cost compared to complex models

Effective with limited and sparse data

Abstract

Reliable and timely dengue predictions provide actionable lead time for targeted vector control and clinical preparedness, reducing preventable diseases and health-system costs in at-risk communities. Dengue forecasting often relies on site-specific covariates and entomological data, limiting generalizability in data-sparse settings. We propose a data-parsimonious (DP) framework based on the incidence versus cumulative cases (ICC) curve, extending it from a basic SIR to a two-population SEIR model for dengue. Our DP model uses only the target season's incidence time series and estimates only two parameters, reducing noise and computational burden. A Bayesian extension quantifies the case reporting and fitting uncertainty to produce calibrated predictive intervals. We evaluated the performance of the DP model in the 2022-2023 outbreaks in Florida, where standardized clinical tests and reporting support accurate case determination. The DP framework demonstrates competitive predictive performance at substantially lower computational cost than more elaborate models, making it suitable for dengue early detection where dense surveillance or long historical records are unavailable.