Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review.

Area of Science:

• Epidemiology
• Public Health
• Environmental Science

Background:

• Vector-borne diseases, such as dengue, pose significant public health challenges.
• Early Warning Systems (EWS) are crucial for timely intervention and minimizing disease impact.
• Dengue EWS development is complex, influenced by human, environmental, vector, and disease-specific factors.

Purpose of the Study:

• To systematically review and discuss the evidence of different Dengue Early Warning Systems (EWS).
• To evaluate the performance and predictive capabilities of various EWS in forecasting dengue outbreaks.
• To identify key indicators and factors contributing to effective dengue EWS.

Main Methods:

• A systematic literature search was conducted across four major databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost.
• Eligible studies were assessed for quality using a standardized checklist.
• A total of 17 studies met the inclusion criteria for the systematic review.

Main Results:

• All reviewed EWS models demonstrated reasonable predictive abilities for dengue outbreaks, though accuracy varied.
• Meteorological indicators (temperature, rainfall) were frequently used and showed the best performance.
• Reported performance metrics included sensitivity (50-100%), specificity (74-94.7%), accuracy (70-96.3%), and PPV (43-86%).

Conclusions:

• Integrating climatic, environmental, epidemiological, and socioeconomic factors enhances EWS effectiveness.
• Meteorological data, alongside entomological, epidemiological, and socioeconomic factors, are vital for robust dengue EWS.
• The efficacy of any EWS is heavily dependent on the quality and timeliness of national surveillance data.