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. 2022;84(2):472-494.
doi: 10.1007/s13571-021-00267-w. Epub 2021 Oct 19.

COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation

Siva Athreya  1 , Giridhara R Babu  2 , Aniruddha Iyer  3 , Mohammed Minhaas B S  3 , Nihesh Rathod  3 , Sharad Shriram  3 , Rajesh Sundaresan  3 , Nidhin Koshy Vaidhiyan  3 , Sarath Yasodharan  3
Affiliations

COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation

Siva Athreya et al. Sankhya B (2008). 2022.

Abstract

We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of C rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate's Fisher-information matrix satisfies a uniform positive definite criterion.

Keywords: COVID-19; Fisher information; adjusted estimate.; c-optimal design; serosurvey; weighted estimate; worst-case design.

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Conflict of interest statement

Conflict of InterestThe authors have no conflicts of interest to declare that are relevant to the content of this article.

Figures

Figure 1
Figure 1
Noise model for the RAT, RT-PCR, and antibody test outcomes. The left-most subfigure is for RAT, j = 1. The middle subfigure is for the RT-PCR test, j = 2. The right-most figure is for the antibody test, j = 3. In each subfigure, the first connection is a “noiseless” channel indicated by M(s, j). In each subfigure, the second channel is a noisy binary asymmetric channel whose correct outcome probabilities are given by the specificity σ(0, j) and sensitivity σ(1, j). The values are indicated in Table 2. The cross-over probabilities are 1 minus these
See this image and copyright information in PMC

References

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    1. Babu, G. R. et al. (2020). The burden of active infection and anti-SARS-cov-2 IgG antibodies in the general population: Results from a statewide survey in Karnataka, India. medRxiv medRxiv 2020.12.04.20243949. - PMC - PubMed
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