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

Milan Stehlik, Jozef Kiselak, A. Dinamarca, E. Alvarado, F. Plaza, F.A. Medina, Silvia Stehlikova, J. Marek, B. Venegas, A. Gajdos, Y. Li, S. Katuscak, A. Brazinova, Y. Lu

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Number of pages35
JournalStochastic Analysis and Applications
DOIs
Publication statusPublished - 2022

Fields of science

  • 101018 Statistics
  • 101024 Probability theory
  • 101026 Time series analysis
  • 101029 Mathematical statistics
  • 102009 Computer simulation
  • 102035 Data science
  • 509 Other Social Sciences

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