Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors
BMC Infectious Diseases·
- DOI
- 10.1186/1471-2334-11-166
- PMID
- 21658238
- PMCID
- PMC3128053
- OpenAlex
- W2026004405
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
SARIMA models incorporating temperature data could be integrated into early warning systems to forecast dengue outbreaks three months in advance, enabling timely implementation of prevention and control activities.
Structured evidence summary
Research question
The study assessed whether incorporating climate variables into time series models could improve prediction of dengue epidemics in Guadeloupe several months in advance to support public health response.
Study design
The authors applied Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling to clinical suspected dengue cases from 2000 to 2006, then validated predictions for 2007 using three forecasting horizons (1 year, 3 months, and 1 month ahead). Climate variables including rainfall, temperature, and relative humidity were incorporated as external regressors.
Population and setting
The study used surveillance data of clinically suspected dengue cases in Guadeloupe, French West Indies, covering the period 2000 to 2007.
Main findings
The 3-month-ahead forecasting approach yielded the most accurate predictions (RMSE = 0.85). Minimum temperature at 5-week lag and average temperature at 11-week lag significantly improved dengue incidence forecasting (p = 0.03 and p = 0.02, respectively), while rainfall showed no correlation. Minimum temperature at 5-week lag best predicted dengue outbreaks (RMSE = 0.72).
Public-health relevance
SARIMA models incorporating temperature data could be integrated into early warning systems to forecast dengue outbreaks three months in advance, enabling timely implementation of prevention and control activities.
Important limitations
This summary relies on the supplied single-article abstract and bibliographic metadata. The full paper is required for decision-grade interpretation of methodological details, model assumptions, and generalizability beyond Guadeloupe.
GIDS interpretation
This article is discoverable through GIDS filters for dengue, Guadeloupe, climate and environment, surveillance, and transmission dynamics. It provides context on climate-based forecasting methods for dengue surveillance but does not itself constitute evidence of a current outbreak or epidemic signal.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 9 auditable classifier relationships to diseases, places, topics, and study design.