REDACS: Regional emergency driven adaptive cluster sampling or effective COVID-19 prevalence

  • Milan Stehlik (Speaker)

Activity: Talk or presentationInvited talkscience-to-science

Description

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. The recent importance of COVID-19 mitigation strategies motivates necessity of scalable, interpretable and precise methodology, which has materialized as REDACS. In this talk we will discuss its feasibility of REDACS implementations. We introduce REDACS: " Regional emergency driven adaptive cluster sampling" for effective COVID-19 prevalence and justify its usage as COVID-19 mitigation strategy. 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, 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.
Period16 Dec 2020
Event titleSCITECH CENTRAL COVID-19 11TH INTERNATIONAL VIRTUAL SEMINAR ON COVID-19- PART II
Event typeConference
LocationAustriaShow on map

Fields of science

  • 101024 Probability theory
  • 106007 Biostatistics
  • 305907 Medical statistics
  • 102009 Computer simulation
  • 509 Other Social Sciences
  • 101018 Statistics
  • 101029 Mathematical statistics
  • 102035 Data science
  • 101026 Time series analysis