The early warning and response systems in Syria: A functionality and alert threshold assessment.

Area of Science:

• Public Health
• Epidemiology
• Health Surveillance

Background:

• Syria faces significant public health challenges, necessitating robust disease surveillance.
• Existing Early Warning Systems (EWS), such as EWARS and EWARN, require ongoing evaluation for optimal functionality.
• Standardized alert thresholds may not be universally applicable across diverse disease profiles.

Purpose of the Study:

• To evaluate the functional characteristics of EWARS and EWARN in Syria.
• To test various World Health Organization (WHO) alert threshold methods against surveillance data for selected diseases.
• To identify optimal alert thresholds for enhancing disease detection and response.

Main Methods:

• Retrospective analysis of EWARN and EWARS surveillance data.
• Application of WHO alert thresholds using three distinct methods for measles, acute bloody diarrhea, acute jaundice syndrome, and severe acute respiratory infections.
• Assessment of threshold suitability using sensitivity, specificity, and Youden index.

Main Results:

• EWARS reported an average of 1,140,717 cases annually, while EWARN reported 10,189,415.
• Optimal alert thresholds varied significantly by disease.
• The percentile method demonstrated strong sensitivity and specificity, with specific optimal thresholds identified for measles (85th), acute bloody diarrhea (75th), and severe acute respiratory infections (90th).

Conclusions:

• The study advocates for adaptable, disease-specific alert thresholds, highlighting the effectiveness of the percentile approach.
• Further development of statistical methods for EWS in conflict zones is recommended.
• Optimized alert thresholds can enhance the sensitivity and specificity of early warning systems for infectious diseases.