From the Public Health Perspective: a Scalable Model for Improving Epidemiological Testing Efficacy in Low- and...

Area of Science:

• Public Health
• Epidemiology
• Global Health Security

Background:

• COVID-19 highlighted global vulnerability to sudden-onset diseases.
• Low- and middle-income areas face unique challenges in epidemic detection due to complex environments, numerous factors, and scarce resources.
• Existing non-pharmacological interventions (NPIs) are often unsuitable for low-resource settings.

Purpose of the Study:

• To explore and propose a strategic model for enhancing disease detection efficacy in low- and middle-income areas.
• To develop an integrated approach for optimizing epidemic surveillance systems in resource-limited settings.
• To address the limitations of current NPIs in low-income contexts.

Main Methods:

• A 3-year case study of SARS-CoV-2 preventive detection in low-income areas of south-central China.
• Development of an integrated model incorporating structural, social, and personal strategies.
• Analysis of epidemic detection and control in resource-scarce environments.

Main Results:

• The proposed model offers an integrated and comprehensive approach to epidemic surveillance.
• Demonstrated potential to improve local epidemic detection efficiency.
• Aimed at ensuring healthcare needs and reducing public health costs in targeted areas.

Conclusions:

• The developed strategic model enhances epidemic surveillance in low- and middle-income areas.
• This approach strengthens local and global public health security sustainably.
• It provides a scalable solution for improving disease detection and control in resource-limited settings.