Recapping the Features of SARS-CoV-2 and Its Main Variants: Status and Future Paths.

Over the two years that we have been experiencing the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic, our challenges have been the race to develop vaccines and the difficulties in fighting against new variants due to the rapid ability of the virus to evolve. In this sense, different organizations have identified and classified the different variants that have been emerging, distinguishing between variants of concern (VOC), variants of interest (VOI), or variants under monitoring (VUM). The following review aims to describe the latest updates focusing on VOC and already de-escalated variants, as well as to describe the impact these have had on the global situation. Understanding the intrinsic properties of SARS-CoV-2 and its interaction with the immune system and vaccination is essential to make out the underlying mechanisms that have led to the appearance of these variants, helping to determine the next steps for better public management of this pandemic.

Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture-Recapture Approaches.

Background and Objectives: Coronavirus disease 19 (COVID-19) has emerged as the most devastating syndemic of the 21st century, with worrisome and sustained consequences for the entire society. Despite the relative success of vaccination programs, the global threat of the novel coronavirus SARS-CoV-2 is still present and further efforts are needed for its containment and control. Essential for its control and containment is getting closer to understanding the actual extent of SARS-CoV-2 infections. Material and Methods: We present a model based on the mortality data of Kazakhstan for the estimation of the underlying epidemic dynamic-with both the lag time from infection to death and the infection fatality rate. For the estimation of the actual number of infected individuals in Kazakhstan, we used both back-casting and capture-recapture methods. Results: Our results suggest that despite the increased testing capabilities in Kazakhstan, official case reporting undercounts the number of infections by at least 60%. Even though our count of deaths may be either over or underestimated, our methodology could be a more accurate approach for the following: the estimation of the actual magnitude of the pandemic; aiding the identification of different epidemiological values; and reducing data bias. Conclusions: For optimal epidemiological surveillance and control efforts, our study may lead to an increased awareness of the effect of COVID-19 in this region and globally, and aid in the implementation of more effective screening and diagnostic measures.

Seropositivity of SARS-CoV-2 in the Population of Kazakhstan: A Nationwide Laboratory-Based Surveillance.

The data on seroprevalence of anti-SARS-CoV-2 antibodies in Kazakhstani population are non-existent, but are needed for planning of public health interventions targeted to COVID-19 containment. The aim of the study was to estimate the seropositivity of SARS-CoV-2 infection in the Kazakhstani population from 2020 to 2021. We relied on the data obtained from the results from "IN VITRO" laboratories of enzyme-linked immunosorbent assays for class G immunoglobulins (IgG) and class M (IgM) to SARS-CoV-2. The association of COVID-19 seropositivity was assessed in relation to age, gender, and region of residence. Additionally, we related the monitoring of longitudinal seropositivity with COVID-19 statistics obtained from Our World in Data. The total numbers of tests were 68,732 for SARS-CoV-2 IgM and 85,346 for IgG, of which 22% and 63% were positive, respectively. The highest rates of positive anti-SARS-CoV-2 IgM results were seen during July/August 2020. The rate of IgM seropositivity was the lowest on 25 October 2020 (2%). The lowest daily rate of anti-SARS-CoV-2 IgG was 17% (13 December 2020), while the peak of IgG seropositivity was seen on 6 June 2021 (84%). A longitudinal serological study should be envisaged to facilitate understanding of the dynamics of the epidemiological situation and to forecast future scenarios.