SARS-CoV-2 Omicron variant genomic and phylogenetic analysis in Iraqi Kurdistan Region

Omicron variants have been classified as Variants of Concern (VOC) by the World Health Organization (WHO) ever since they first emerged as a result of a significant mutation in this variant, which showed to have an impact on transmissibility and virulence of the virus, as evidenced by the ongoing modifications in the SARS-CoV-2 virus. As a global pandemic, the Omicron variant also spread among the Kurdish population. This study aimed to analyze different strains from different cities of the Kurdistan region of Iraq to show the risk of infection and the impact of the various mutations on immune responses and vaccination. A total of 175 nasopharyngeal/oropharyngeal specimens were collected at West Erbil Emergency Hospital and confirmed for SARS-CoV-2 infection by RT-PCR. The genomes of the samples were sequenced using the Illumina COVID-Seq Method. The genome analysis was established based on previously published data in the GISAID database and compared to previously detected mutations in the Omicron variants, and that they belong to the BA.1 lineage and include most variations determined in other studies related to transmissibility, high infectivity and immune escape. Most of the mutations were found in the RBD (receptor binding domain), the region related to the escape from humoral immunity. Remarkably, these point mutations (G339D, S371L, S373P, S375F, T547K, D614G, H655Y, N679K and N969K) were also determined in this study, which were unique, and their impact should be addressed more. Overall, the Omicron variants were more contagious than other variants. However, the mortality rate was low, and most infectious cases were asymptomatic. The next step should address the potential of Omicron variants to develop the next-generation COVID-19 vaccine.

Risk of death, hospitalization and intensive care unit admission by SARS-CoV-2 variants in Peru: a retrospective study

Objectives: Peru has had the highest death toll from the pandemic worldwide;however, it is not clear what the effects of the different variants on these outcomes are. The study aimed to evaluate the risk of death, hospitalization, and intensive care unit (ICU) admission rates of COVID-19 according to the SARS-CoV-2 variants detected in Peru from March 2020-February 2022. Methods: Retrospective study using open-access databases were published by the Peruvian Ministry of Health. Databases of genomic sequencing, death, COVID-19 cases, hospitalization and ICU, and vaccination were used. Crude and adjusted Cox proportional hazards regressions with clustered variances were modeled to calculate the hazard ratio (HR) of outcomes by variant. Results: Lambda variant had the highest risk of death (HR 1.92, 95% CI 1.37-2.68), whereas the Delta variant had the lowest risk (HR 0.50, 95% CI 0.31-0.82). Mu variant had the highest risk of hospitalization (HR: 2.39, 95% CI 1.56-3.67), Omicron the lowest (HR 0.45, 95%CI 0.23-0.90), and Gamma had the highest ICU admission rate (HR 1.95, 95%CI 1.40-2.71). Conclusion: SARS-CoV-2 variants showed distinctive risks of clinical outcomes, which could have implications for the management of infected persons during the pandemic.

COVID-19 and the pathologies of Australia's regulatory state

The COVID-19 pandemic has elicited a wide range of national responses with an even wider range of outcomes in terms of infections and mortalities. Australia is a rare success story, keeping deaths comparatively low, and infections too, until the Omicron wave. What explains Australia's success? Typical explanations emphasise leaders' choices. We agree, but argue that leaders' choices, and whether these are implemented effectively, is shaped by the legacy of state transformation. Decades of neo-liberal reforms have hollowed out state capacity and confused lines of control and accountability, leaving Australia unprepared for the pandemic. Leaders thus abandoned plans and turned to ad hoc, simple to implement emergency measures - border closures and lockdowns. These averted large-scale outbreaks and deaths, but with diminishing returns as the Delta variant took hold. Conversely, Australia's regulatory state has struggled to deliver more sophisticated policy responses, even when leaders were apparently committed, including an effective quarantine system, crucial for border controls, and vaccination programme, essential for exiting the quagmire of lockdowns and closed borders, leading to a partial return to top-down governing. The Australian experience shows that to avoid a public health catastrophe or more damaging lockdowns in the next pandemic, states must re-learn to govern.

Twice evasions of omicron variants explain the temporal patterns in six Asian and oceanic countries

Background: The ongoing coronavirus 2019 (COVID-19) pandemic has emerged and caused multiple pandemic waves in the following six countries: India, Indonesia, Nepal, Malaysia, Bangladesh and Myanmar. Some of the countries have been much less studied in this devastating pandemic. This study aims to assess the impact of the Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the reproduction number [Formula: see text] in these six South Asia, Southeast Asia and Oceania countries. Methods: We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate [Formula: see text] to fit the multiple waves of the COVID-19 pandemic and to estimate the IFR and [Formula: see text] in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of the Omicron variant are also considered in this model. Results: We fit our model to the reported deaths well. We estimate the IFR (in the range of 0.016 to 0.136%) and the reproduction number [Formula: see text] (in the range of 0 to 9) in the six countries. Multiple pandemic waves in each country were observed in our simulation results. Conclusions: The invasion of the Omicron variant caused the new pandemic waves in the six countries. The higher [Formula: see text] suggests the intrinsic transmissibility advantage of the Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and vaccine coverage.

Empirical evidence of effects of stringency amid Covid-19 pandemic spread

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Relationship Between a State-Directed Lockdown on Non-COVID-19-related ICU Admissions and Mortality in a Multicenter Colorado Healthcare System.

Colorado issued a month long statewide lockdown on March 26, 2020, during the initial surge of the COVID-19 pandemic. The impact of this mandate on non-COVID-19 ICU admission rates and outcomes is unclear. DESIGN: We performed a retrospective analysis of all medical ICU admissions in the University of Colorado Health System in four predefined periods: 1) prepandemic (2 mo prior to lockdown period 1); 2) mandated lockdown from March 26 to April 26, 2020 (period 2); 3) between surges (period 3); and 4) nonmandated lockdown surge (between November 1, 2020, and March 31, 2021, period 4). SETTING: Nonsurgical ICU admissions at the University of Colorado Health Systems, including 10 hospitals throughout Colorado. SUBJECTS: All ICU admissions in four predefined time periods. MEASUREMENTS AND MAIN RESULTS: We included 13,787 patients who were admitted during the four study periods. The 28-day mortality rates for non-COVID-19 ICU admissions following index ICU admission were 13.6%, 18.0%, 13.5%, and 16.0% across periods 1-4, respectively. However, the increased odds in non-COVID-19 ICU mortality during the mandated lockdown period relative to prepandemic 1 (odds ratio [OR], 1.39; 95% CI, 1.11-1.72; p = 0.0.04) was attenuated and nonsignificant after adjustment for demographics, comorbidities, diagnosis flags, and severity (OR, 1.15; 95% CI, 0.89-1.48; p = 0.27). Similar results were found in time-to-event analyses. The most common diagnosis in each time period was acute respiratory failure (ARF), and we found it to have increased during lockdown (p < 0.001), whereas sepsis admissions increased during and decreased after lockdown (p = 0.004). Admissions for alcohol withdrawal syndrome (AWS) increased during lockdown and 6 months afterwards (p = 0.005). CONCLUSIONS: For non-COVID-19-related ICU admissions, mortality rate was similar before, during, and after Colorado's month long lockdown after confounder adjustment, including typical ICU admission flags. Primary admission diagnoses shifted throughout the predefined study periods with more admissions for severe critical diagnoses (i.e., ARF, sepsis, AWS) occurring during the mandated lockdown and nonmandated lockdown periods compared with the prepandemic and between surge period. This would suggest that the perceived increase in mortality during the lockdown for non-COVID-19 ICU admissions may be related to a shift inpatient demographics.

Do prior neurological comorbidities predict COVID-19 severity and death? A 25-month cross-sectional multicenter study on 7370 patients.

BACKGROUND: The prognosis of COVID-19 cases that suffer from particular comorbidities is worse. The impact of chronic neurological disorders (CNDs) on the outcome of COVID-19 patients is not clear yet. This study aimed to assess whether CNDs can predict in-hospital mortality or severity in COVID-19 patients. METHODS: Following a cross-sectional design, all consecutive hospitalized patients with PCR-confirmed COVID-19 who were hospitalized at three centers from February 20th, 2020 to March 20th, 2022, were studied. CND was defined as neurological conditions resulting in permanent disability. Data on demographic and clinical characteristics, COVID-19 severity, treatment, and laboratory findings were evaluated. A multivariate Cox-regression log-rank test was used to assess the primary outcome, which was in-hospital all-cause mortality. The relationship among CND, COVID-19 severity and abnormal laboratory findings was analyzed as a secondary endpoint. RESULTS: We studied 7370 cases, 43.6% female, with a mean age of 58.7 years. 1654 (22.4%) patients had one or more CNDs. Patients with CNDs had higher age, were more disabled at baseline, and had more vascular risk factors and comorbidities. The ICU admission rate in CND patients with 59.7% was more frequent than the figure among non-CND patients with 20.3% (p = 0.044). Mortality of those with CND was 43.4%, in comparison with 12.8% in other participants (p = 0.005). Based on the Cox regression analysis, CND could independently predict death (HR 1.198, 95% CI 1.023-3.298, p = 0.003). CONCLUSION: CNDs could independently predict the death and severity of COVID-19. Therefore, early diagnosis of COVID-19 should be considered in CND patients.

Effect of Population Density and Economic Indicators on COVID-19 Death Rates in the Community of Latin American and Caribbean States.

OBJECTIVE: The countries of the Community of Latin American and Caribbean States (CELAC, by its initials in Spanish) have been some of the most affected by COVID-19. This paper analyzes whether, in the 33 CELAC countries, population density, together with other economic variables, such as gross domestic product (GDP) at purchasing power parity (PPP) values or the Human Development Index (HDI), were significantly associated with the coronavirus mortality rate. METHODS: A correlation analysis and an ordinary least squares regression model were used to analyze the effects of different variables on the COVID-19 mortality rate. RESULTS: The results showed that countries with higher numbers of inhabitants per square kilometer had lower death rates. Gross domestic product was not associated with the number of deaths, while the HDI had a positive impact on that number. CONCLUSION: Countries with high population density are not more vulnerable to COVID-19, as population density allows for economic development and better-designed institutions.