Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case...

Area of Science:

• Epidemiology and Public Health
• Infectious Disease Modeling
• Risk Assessment

Background:

• Epidemics pose significant global economic and social threats.
• Current infectious disease control often relies on emergency response rather than proactive measures.
• A scalable, open-data-driven epidemic risk index is needed for early detection and prevention.

Purpose of the Study:

• To introduce and validate a methodology for constructing a scalable epidemic risk index using open data.
• To apply this index to assess dengue risk in the Philippines.
• To demonstrate the index's utility for public health decision-making.

Main Methods:

• Developed a methodology for an epidemic risk index using open-source data.
• Employed distance sampling to correct for underreporting of infections based on healthcare accessibility.
• Validated the index against epidemiological metrics like dengue incidence and case fatality rate (CFR).

Main Results:

• The computed dengue risk index showed a significant correlation with dengue incidence (p = 0.002).
• Susceptibility was a key dimension, correlating with incidence (p = 0.048) and CFR (p = 0.029).
• Highest dengue risk was identified in southern Philippines, linked to high susceptibility.

Conclusions:

• The developed epidemic risk index accurately reflects sub-national dengue disease patterns.
• The methodology provides a practical, evidence-based tool for disease risk assessment, especially when timely data is scarce.
• This index enhances public health and humanitarian decision-making through simple, actionable metrics.