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. 2023;41(3):474-508.
doi: 10.1080/07362994.2022.2033126. Epub 2022 Feb 25.

REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management

M Stehlík  1   2   3 , J Kisel'ák  4 , A Dinamarca  5 , E Alvarado  2 , F Plaza  2   6 , F A Medina  7 , S Stehlíková  1 , J Marek  8 , B Venegas  9 , A Gajdoš  3 , Y Li  10 , S Katuščák  11 , A Bražinová  12 , E Zeintl  1 , Y Lu  13
Affiliations

REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management

M Stehlík et al. Stoch Anal Appl. 2023.

Abstract

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of "on the fly" biomarker validation, i.e., during the screening.

Keywords: ACS; REDACS; antigen test validation; prevalence; sampling.

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Figures

Figure 8.
Figure 8.
Idealized relationships between health outcome, biomarker status and treatment, for three different types of biomarkers. Plots show health outcome as a function of treatment (C = control, T = therapeutic) and biomarker status (solid line = negative, dashed line = positive). (A) A biomarker that is only prognostic. The biomarker is not predictive in the sense that treatment effect is the same regardless of biomarker status. (B) A biomarker that is only predictive as only one of the biomarker groups benefits from treatment. (C) A biomarker that is both prognostic and predictive; biomarker-positive patients have better outcomes than biomarker negative patients regardless of treatment group, and treatment effect is different depending on the biomarker level. Inspired by Figure 2 from [50].
Figure 1.
Figure 1.
Active cases in Slovakia from 23.10.2020: Districts (Left), Regions (Right).
Figure 2
Figure 2
Map of Chile and its 16 respective regions.
Figure 3.
Figure 3.
Selected variables and their spatial relationship: Smoking percentage (a), Temperature (b), Primary, Secondary and Terciary workers (c, d and e, respectively), and Household income (f).
Figure 4.
Figure 4.
Lyapunov exponents for system (10) with μ=0.05, Δt=1, β=1.4, N=1000.
Figure 5.
Figure 5.
Active cases.
Figure 6.
Figure 6.
Daily new cases.
Figure 7.
Figure 7.
Lyapunov exponent vs. lag.
See this image and copyright information in PMC

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