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Adaptive Cluster Sampling-Based Design for Estimating COVID-19 Cases With Random Samples


Affiliations
1 Division of Forestry Statistics, Indian Council of Forestry Research and Education, Dehradun 248 006, India
2 Department of Statistics, Kumaun University, SSJ Campus, Almora 263 601, India
 

During the COVID-19 pandemic, testing of all persons except those who are symptomatic, is not feasible due to shortage of facilities and staff. This article focuses on estimating the number of COVID-19-positive persons over a geographical domain. The Horvitz–Thompson and Hansen–Hurwitz type estimators under adaptive cluster sampling-based design have been suggested. Two case studies are discussed to demonstrate the performance of the estimators under certain assumptions. Advantages and limitations are also mentioned.

Keywords

Adaptive Cluster Sampling, COVID-19, Pandemic, Precise Estimation, Random Samples.
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  • Thompson, S. K., Adaptive cluster sampling. J. Am. Stat. Assoc., 1990, 85, 1050–1059.

  • Thompson, S. K., Adaptive cluster sampling based on order statistics. Environmetrics, 1996, 7(2), 123–133.

  • Chandra, G., Tiwari, N. and Nautiyal, R., Two stage adaptive cluster sampling based on ordered statistics. Metodoloski Zv., 2019, 16(1), 43–60.

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  • Adaptive Cluster Sampling-Based Design for Estimating COVID-19 Cases With Random Samples

Abstract Views: 356  |  PDF Views: 139

Authors

Girish Chandra
Division of Forestry Statistics, Indian Council of Forestry Research and Education, Dehradun 248 006, India
Neeraj Tiwari
Department of Statistics, Kumaun University, SSJ Campus, Almora 263 601, India
Raman Nautiyal
Department of Statistics, Kumaun University, SSJ Campus, Almora 263 601, India

Abstract


During the COVID-19 pandemic, testing of all persons except those who are symptomatic, is not feasible due to shortage of facilities and staff. This article focuses on estimating the number of COVID-19-positive persons over a geographical domain. The Horvitz–Thompson and Hansen–Hurwitz type estimators under adaptive cluster sampling-based design have been suggested. Two case studies are discussed to demonstrate the performance of the estimators under certain assumptions. Advantages and limitations are also mentioned.

Keywords


Adaptive Cluster Sampling, COVID-19, Pandemic, Precise Estimation, Random Samples.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi7%2F1202-1210