Assessing population-sampling strategies for reducing the COVID-19 incidence

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Achieving herd immunity for SARS-CoV-2 remains challenging due to vaccination gaps and evolving viral strains.
• Infection speed reduction relies on tracking infected individuals to prevent transmission.
• Population sampling has demonstrated efficacy in mitigating epidemic spread.

Purpose of the Study:

• To refine a scalable agent-based COVID-19 simulator with enhanced socio-demographic and population mixing models.
• To develop and evaluate diverse population sampling strategies for COVID-19 control in Spain's major cities.
• To analyze the impact of various parameters on sampling efficacy.

Main Methods:

• Extended agent-based COVID-19 simulator incorporating profession-based demographics and country-specific contact matrices.
• Simulation of sampling strategies across 62 major Spanish cities.
• Analysis of parameters including testing frequency, quarantine duration, quarantine breaker rates, and group testing.

Main Results:

• Pooling, rapid antigen test campaigns, and mandatory negative testing for public access emerged as highly effective sampling strategies.
• Reducing contact rates for infected individuals significantly amplifies the effectiveness of all tested strategies.
• The refined simulator provides a robust platform for evaluating public health interventions.

Conclusions:

• Targeted population sampling, particularly through pooling and rapid testing, is crucial for managing COVID-19 spread.
• Behavioral interventions, such as contact reduction, are vital for maximizing the impact of testing strategies.
• The study offers valuable insights for optimizing public health responses to infectious disease outbreaks.