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REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management
M Stehlík1,2,3, J Kisel'ák4, A Dinamarca5
1Linz Institute of Technology & Department of Applied Statistics, J. Kepler University in Linz, Linz, Austria.
Stochastic Analysis and Applications
|November 20, 2023
Summary
National agencies require advanced sampling strategies for COVID-19 surveillance. Regional emergency-driven adaptive cluster sampling (REDACS) offers an effective solution, outperforming traditional methods by accounting for spatial heterogeneity and enabling real-time adjustments.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- Effective COVID-19 management necessitates robust monitoring and tracking of key epidemiological metrics.
- Current testing programs often lack the precision required for accurate prevalence studies and control strategies.
- Spatial and regional variations in disease spread significantly impact the efficacy of sampling plans.
Purpose of the Study:
- To introduce and justify the use of Regional Emergency-driven Adaptive Cluster Sampling (REDACS) for COVID-19 surveillance and control.
- To demonstrate the advantages of REDACS over traditional, large-scale individual testing sampling plans.
- To highlight the importance of adaptive sampling strategies that incorporate real-time data from frontline health services.
Main Methods:
- Development and theoretical justification of REDACS, a novel adaptive cluster sampling methodology.
- Comparative analysis of REDACS against conventional sampling methods for prevalence studies.
- Illustration of REDACS using spatial heterogeneity data from Chile, considering its COVID-19 winter outbreak peak.
- Exploration of the link between regional heterogeneity, microbiological factors, and Lyapunov exponents.
Main Results:
- REDACS provides a more effective approach to COVID-19 prevalence studies and management compared to massive individual testing.
- The strategy effectively addresses regional and spatial heterogeneity inherent in disease transmission.
- Adaptive control parameters derived from emergency health stations are crucial for dynamic management.
- Antigen test screening is discussed for its potential in "on the fly" biomarker validation.
Conclusions:
- REDACS offers a superior framework for COVID-19 surveillance, particularly in diverse geographical and epidemiological contexts.
- Adaptive sampling strategies are essential for optimizing resource allocation and response during pandemics.
- Understanding and incorporating regional heterogeneity is fundamental for successful disease control.
- The methodology supports dynamic adjustments based on real-time data, enhancing public health interventions.
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