Analysis of COVID-19 pandemics in Kazakhstan

RT-qPCR investigation of post-mortem tissues during COVID-19

In 2020, there were numerous cases in Kazakhstan with clinical symptoms of COVID-19 but negative PCR results in nasopharyngeal and oropharyngeal swabs. The diagnosis was confirmed clinically and by CT scans (computed tomography). The problem with such negative PCR results for SARS-CoV-2 infection confirmation still exists and indicates the need to confirm the diagnosis in the bronchoalveolar lavage in such cases. There is also a lack of information about confirmation of SARS-CoV-2 infection in deceased patients. In this study, various tissue materials, including lungs, bronchi, and trachea, were examined from eight patients who died, presumably from SARS-CoV-2 infection, between 2020 and 2022. Naso/oropharyngeal swabs taken from these patients in hospitals tested PCR negative for SARS-CoV-2. This study presents a modified RNA isolation method based on a comparison of the most used methods for RNA isolation in laboratories: QIAamp Viral RNA Mini Kit and TRIzol-based method. This modified nucleic acid extraction protocol can be used to confirm SARS-CoV-2 infection by RT-qPCR in the tissues of deceased patients in disputed cases. RT-qPCR with RNA of SARS-CoV-2 re-extracted with such method from post-mortem tissues that were stored at -80 °C for more than 32 months still demonstrated high-yielding positive results.

Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan

BACKGROUND

Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach.

METHOD

We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data.

RESULTS

The real data of total cases turned out to be outside the predicted minimum-maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term.

CONCLUSIONS

In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of β(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters.

Impact of the SARS-CoV-2 Outbreak on the Epidemiology and Treatment Outcomes of Fractures of the Proximal Femur in Kazakhstan

The study aimed to assess the impact of isolation and quarantine on the frequency of registration and the treatment of fractures of the proximal femur in Kazakhstan in the context of the COVID-19 pandemic in 2020 (compared to the pre-pandemic period). This retrospective observational comparative study included all primary patients with injuries (the code S72) in the period 2019-2020 according to the national register.

In 2020, the number of S72 fractures was 6.6 % higher compared to 2019. In comparison with 2019, in 2020 the number of beddays of patients was reduced to 7.1±3.8 days (p≤0.001). Both in 2019 and in 2020, the number of women predominated among all patients (p ≤ 0.05). The frequency of conservative treatment in 2020 compared to 2019 was increased from 26.6% to 35.6%, while the surgical procedure for internal fixation was reduced to 34.2% in 2020. In 2020, the highest number of cases among women with S72 fractures cases were recorded in the age groups 60-74 years and 75-90 years. In 2019 in female patients (42%) with S72 cases were registered in the age group 75-90 years.

The incidence of fractures of the proximal femur did not change significantly in 2020 compared to 2019. However, the number of conservative treatment methods has increased along with the decrease in the frequency of surgical interventions. We observed the growth of the frequency of non-surgical treatment methods in 2020 that might impose the possible risks of mortality of these patients in the long term after conservative treatment.

COVID-19 Mortality in Public Hospitals in a Brazilian State: An Analysis of the Three Waves of the Pandemic

OBJECTIVE

To analyze COVID-19 deaths in public hospitals in a Brazilian state, stratified by the three waves of the pandemic, and to test their association with socio-clinical variables.

METHODS

Observational analytical study, where 5436 deaths by COVID-19 occurred in hospitals of the public network of Espírito Santo, between 1 April 2020, and 31 August 2021, stratified by the three waves of the pandemic, were analyzed. For the bivariate analyses, the Pearson's chi-square, Fisher's Exact or Friedman's tests were performed depending on the Gaussian or non-Gaussian distribution of the data. For the relationship between time from diagnosis to death in each wave, quantile regression was used, and multinomial regression for multiple analyses.

RESULTS

The mean time between diagnosis and death was 18.5 days in the first wave, 20.5 days in the second wave, and 21.4 days in the third wave. In the first wave, deaths in public hospitals were associated with the following variables: immunodeficiency, obesity, neoplasia, and origin. In the second wave, deaths were associated with education, O₂ saturation < 95%, chronic neurological disease, and origin. In the third wave, deaths were associated with race/color, education, difficulty breathing, nasal or conjunctival congestion, irritability or confusion, adynamia or weakness, chronic cardiovascular disease, neoplasms, and diabetes mellitus. Origin was associated with the outcome in the three waves of the pandemic, in the same way that education was in the second and third waves (p < 0.05).

CONCLUSION

The time interval between diagnosis and death can be impacted by several factors, such as: plasticity of the health system, improved clinical management of patients, and the start of vaccination at the end of January 2021, which covered the age group with the higher incidence of deaths. The deaths occurring in public hospitals were associated with socio-clinical characteristics.

Sero‐prevalence of SARS‐CoV‐2 in certain cities of Kazakhstan

Background and Aims

Seroprevalence studies are needed to determine the cumulative prevalence of SARS‐CoV‐2 infection and to develop pandemic mitigation strategies. Despite the constant monitoring and surveillance, the true level of infection in the population of Kazakhstan remains unknown. The aim of this study was to determine the sero‐prevalence of SARS‐CoV‐2 in the main cities of Kazakhstan.

Methods

The research was conducted as a cluster‐randomized cross‐sectional national household study in three cities of Kazakhstan. The study covered the period: from October 24, 2020, to January 11, 2021. A total of 5739 people took part in the study. All participants agreed to be tested for antibodies to IgM/IgG. Demographic characteristics were analyzed. The presence of symptoms of respiratory diseases and the results of polymerase chain reaction (PCR) testing were determined. The antibodies to the SARS‐CoV‐2 virus were detected using the method of enzyme‐linked immunosorbent assay (ELISA).

Results

There was significant geographic variability with a higher prevalence of IgG/IgM antibodies to SARS‐CoV‐2 in Almaty 57.0%, in Oskemen 60.7% than in Kostanay 39.4%. There were no significant differences in prevalence between men and women (p ≥ 0.05). In Almaty, only 19% of participants with antibodies reported the presence of respiratory symptoms during a pandemic. At the same time, the percentage of patients with antibodies who had respiratory symptoms was 36% in Oskemen and 27% in Kostanay.

Conclusion

The findings indicate that despite reasonable level of seroprevalence, the country has not yet reached the baseline minimum of herd immunity scores. The prevalence estimates for asymptomatic or subclinical forms of the disease ranged from 64% to 81%. Thus, given that almost half of the population of Kazakhstan remains vulnerable, the importance of preventive strategies such as social distancing, the use of medical masks, and vaccination to protect the population from the transmission of SARS‐CoV‐2 is highly critical.