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

DOI10.1080/07362994.2022.2033126zbMath1510.62455OpenAlexW4214664038MaRDI QIDQ6046008

No author found.

Publication date: 15 May 2023

Published in: Stochastic Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07362994.2022.2033126

zbMATH Keywords

samplingprevalenceACSantigen test validationREDACS

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05) Medical epidemiology (92C60)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On convergence of topological aggregation functions
• Estimating the dimension of a model
• Some discrete-time SI, SIR, and SIS epidemic models
• Unifying the derivations for the Akaike and corrected Akaike information criteria.
• An optimization and simulation approach to emergency stations relocation
• A practical method for calculating largest Lyapunov exponents from small data sets
• Adaptive Cluster Sampling
• A note on a general definition of the coefficient of determination
• Period Three Implies Chaos
• An Introduction to Statistical Learning
• On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision
• Covariate-Assisted Ranking and Screening for Large-Scale Two-Sample Inference

This page was built for publication: REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management