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. 2021 Nov 11;10(11):1464.
doi: 10.3390/pathogens10111464.

The Predictive Value of Mutation Screening for Anticipating COVID-19 Waves

Robert Hohan  1 , Petre Milu  1   2 , Simona Paraschiv  1   2 , Corina Casangiu  1   3 , Andreea Tudor  4 , Ovidiu Vlaicu  1 , Leontina Banica  1 , Marius Surleac  1   5 , Dragos Florea  1   2 , Dan Otelea  1
Affiliations

The Predictive Value of Mutation Screening for Anticipating COVID-19 Waves

Robert Hohan et al. Pathogens. .

Abstract

Emerging SARS-CoV-2 strains continue to generate difficulties for authorities and health care professionals worldwide due to enhanced transmissibility and/or immune response evasion. The appearance of the Alpha and Delta strains has been associated with substantial increases in the number of COVID-19 cases and associated deaths. Whole Genome Sequencing (WGS) continues to be the gold standard for molecular surveillance of the pandemics but other assays such as mutation genotyping can be used to reduce costs and allocated time. This study investigates the efficiency of mutation screening tests compared to WGS and their predictive value to anticipate future waves. A very high degree of fidelity for this type of assay was found, regardless of the method used. The positive predictive value (PPV) of 4/5 markers was over 95% for the detection of Alpha and Delta variants. By estimating the prevalence of the Alpha and Delta strains using genotyping assays and fitting the data to a mathematical model, a five week period between the point of exponential growth of variant prevalence and a drastic increase in case numbers was found. For that reason, raising awareness about the efficacy of mutation screening could help authorities adopt better measures in the future.

Keywords: infection; mutation; prediction; prevalence; screening.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The timeline (expressed in weeks, with each point being formatted as year_week) of the prevalence of the two VOCs under consideration: Alpha (blue) and Delta (orange). The shaded areas represent the period of exponential growth for each variant respectively as calculated from the model fit.
Figure 2
Figure 2
Prevalence data (fitted to the Baranyi model) and number of new infections. To normalise the values for the new weekly infections the number was divided by the maximum number of infections. The four labelled time-points are: A (start of the exponential phase for Alpha VOC), B (beginning of the third COVID-19 wave), C (start of the exponential phase for Delta VOC), and D (beginning of the fourth COVID-19 wave).
See this image and copyright information in PMC

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