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

2025-11-26

AI Summary

Early dengue fever warning in data-sparse regions remains challenging due to limited surveillance infrastructure and scarce covariate data. Method: This paper proposes a minimal two-parameter time-series-only model grounded in a Data-Parsimonious (DP) framework. It integrates incidence curve characterization (ICC analysis), a two-group SEIR dynamical structure, and Bayesian uncertainty quantification to ensure parameter identifiability and robustness—requiring only two estimable parameters, thereby reducing noise sensitivity and computational overhead while yielding well-calibrated prediction intervals. Results: Evaluated on the 2022–2023 Florida dengue outbreak, the model achieves short-term forecasting accuracy comparable to complex covariate-dependent models, yet with over an order-of-magnitude reduction in computational cost. Its core contribution is the first integration of ICC-based curve analysis with a parsimonious SEIR formulation to establish a purely time-series-driven Bayesian dengue forecasting paradigm—delivering a highly practical, real-time monitoring and intervention tool for resource-constrained settings.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Develops a minimal-data model for dengue case prediction
• Addresses generalizability issues in data-sparse outbreak settings
• Enables early detection with low computational and data requirements

Innovation

• Two-parameter SEIR model using incidence-cumulative curve
• Bayesian extension quantifies reporting and fitting uncertainty
• Data-parsimonious framework reduces noise and computational burden