Mental health status survey in the first⁃line healthcare workers of designated hospitals for COVID⁃19 under the delta strain outbreak

Objective: To investigate mental health status of first-line healthcare workers in designated hospitals for coronavirus disease 2019 (COVID-19) under the Delta strain outbreak, so as to provide a basis for the implementation of effective countermeasures. Methods: We conducted an online survey on mental health status among 227 first-line medical staff treating COVID-19 patients in Wuhan Jinyintan Hospital during the period from September 4 to 6, 2021 using Self-Rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS). Results：The surveyed first-line medical staff had significantly higher levels of anxiety and depression than the national norms (P<0. 01), and among them, 54 (23. 70%) individuals had anxiety, 33(14. 5%) had depressive emotions, and 26 (11. 5%) had both. Nurses or female staff scored significantly higher than clinicians or male staff for anxiety and depression (P<0. 01). Married or middle-aged and young (30 to 45 years old) first-line medical staff had the relatively high scores of depression and anxiety (P<0. 01). Anxiety and depression of medical staff were associated with the time of working in the first-line, and those who continued to work 10-20 days had the relatively high scores of depression and anxiety (P<0. 05). Conclusion: In the face of SARS-CoV -2 Delta variant outbreak, the first-line medical staff still had negative mentality, such as anxiety and depression, psychological intervention support system should be established in order to help improve the level of the first-line medical staff's mental health and improve the working condition. © 2022 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

Modifying impact of environmental factors on the course of an epidemic process

Introduction. It is necessary to establish peculiarities and regularities of COVID-19 infection;this task requires further research on how to formalize and build spatial-temporal models of the infection spread. This article focuses on determining non-infectious factors that can modify the epidemic process caused by the COVID-19 coronavirus for further substantiation of integrated solutions that are necessary to ensure sanitary-epidemiological welfare of the RF population. Materials and methods. Our study involved analyzing regularities of regional differentiation in parameters introduced into mathematical models. These models described how the epidemic process developed in RF regions depending on modifying non-infectious factors identified by modelling the dynamics of spread of SARS-CoV-2 delta strain. These modifying factors included anti-epidemic activities;sanitary-epidemiological, sociodemographic, and economic conditions in a region;weather and climate;public healthcare systems and people's lifestyles in RF regions over 2020–2021. The dynamics of the epidemic process was modelled by using the conventional SIR-model. Relationships between parameters introduced into the model of the epidemic process and modifying regional conditions were examined by using correlation-regression analysis. Results. The modelling made it possible to identify priority risk factors that modified COVID-19 spread authentically (p<0.05) and explained regional differences in intensity of contagion, recovery and lethality. We established that population coverage with vaccination, especially among people aged 31–40 years, had the greatest authentic positive influence on the decline of reproduction index (R0) of the virus (r=–0.37). An increase in monthly average temperatures in autumn and winter as well as over a year made for people moving faster from the susceptible to infected category (r=0.21–0.22). Growing sun insolation over a year, especially in summer, resulted in slower movement of susceptible people into the infected category (r=–0.02–(–0.23)). Next, several sanitary-epidemiological indicators authentically made the infection spread faster;they were improper working conditions (not conforming to the safety standards as per physical indicators) and ambient air quality in settlement not corresponding to the hygienic standards as per chemical indicators and noise (r=0.29–0.24). Recovery took longer in regions where alcohol consumption was comparatively higher (r=–0.32). Limitations. The limitations of the study include modelling the epidemic process using the standard SIR model;limited set of indicators and period of analysis. Conclusions. The existing regional differentiation in development of specific stages in the epidemic process related to the COVID-19 delta strain occurs due to complex interactions and influence exerted by modifying factors that create a certain multi-level and multi-component system. This system is able to transform the epidemic process either potentiating it or slowing it down. © 2022 Izdatel'stvo Meditsina. All rights reserved.

Vaccination efficacy against post-COVID-19 symptoms in Delta and Omicron waves: a prospective cohort in East Indonesia

The post COVID-19 symptoms affect the productivity and the quality of life among survivors. It is imperative to identify the effect of virus variants and the vaccination against post-COVID-19 symptoms. There were 242 participants from the eastern part of Indonesia diagnosed with COVID-19 during July 2021-July to 2022 were recruited online. The participants underwent data collection and semi-clinical follow-up for post-COVID-19 symptoms within 30 days after the first symptoms or from the diagnosis day using a validated clinical questionnaire and physician confirmation. Fatigue was the most reported post-COVID-19 symptom (27.7%), followed by chronic cough (21.5%) and headache (15.3%). Adjusted by confounding factors in hierarchical logistic regression, the differences in post-COVID-19 symptoms were insignificant across different variants. Regarding vaccine efficacy against three post-COVID-19 symptoms, people with two-dose vaccination significantly reported lower post-COVID-19 chronic cough (adjusted Odds Ratio 0.244 95% CI OR 0.071-0.838), but the protection against fatigue and the chronic headache was insignificant. There is an indication that vaccine efficacy may be waning along with the emerging new variants. © 2023, Intelektual Pustaka Media Utama. All rights reserved.

Immune Response after 2 Doses of BNT162b2 mRNA COVID-19 Vaccinations in Children and Adolescents with Cancer and Hematologic Diseases.

BACKGROUND: The BNT162b2 mRNA COVID-19 vaccine has been administered to children and adolescents with cancer and hematologic diseases since they are at high risk of manifesting severe symptoms if they have COVID-19 infection but the adequate immune response after vaccination in these immunocompromised patients are questionable. OBJECTIVE: To evaluate the immune response of children and adolescents with cancer and hematologic diseases after receiving 2 doses of the BNT162b2 mRNA COVID-19 vaccine. METHODS: This is a prospective cohort study of patients with cancer and hematologic disease, who aged 12- 18 years old and received 2 doses the BNT162b2 vaccines at 4 weeks apart were enrolled. Immunogenicity was determined by measuring serum anti-SARS-CoV-2 immunoglobulin antibodies directed against the receptor binding domain (RBD) of S1 domain of the spike protein (Anti S-RBD), surrogated viral neutralization test (sVNT) of SARS-CoV-2 and Delta strain. Blood samples were collected and analyzed at 4 and 12 weeks after vaccination. The seroprotective rate was defined as sVNT ≥ 68%. RESULTS: From Oct 2021 to Jan 2022, 43 children were enrolled, 21 were on-therapy and 22 were off-therapy. 25 were hematologic malignancy, 15 solid tumor and 3 hematologic diseases with immunosuppressive drugs. The GMT (95%CI) of a anti S-RBD IgG level at 4 weeks after vaccination were 56.05 (13.2,238.2) and 3633 (2689,4908) BAU/mL in on-therapy and off-therapy group, respectively, p<0.001. The sVNT (95%CI) of delta strain were 26% (5.85-73.55%) and 97.05% (96.0-97.4%) as the seroprotective level which were 33.3% in on-therapy group and 100% in off-therapy group (p<0.001). 14 children in on-therapy group need an additional dose. CONCLUSION: After complete vaccination, the seroprotective rate and antibody level in pediatric and adolescent patients with cancer and hematologic disease who receive immunosuppressive agents are quite low, compared with patients who had complete treatment. Additional dose of primary series should be offered.

Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant.

BACKGROUND: The Delta (B.1.617.2) SARS-CoV-2 variant was the predominant UK circulating strain between May and November 2021. We investigated whether COVID-19 from Delta infection differed from infection with previous variants in children. METHODS: Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between 28 December 2020 and 8 July 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: 28 December 2020 to 6 May 2021 (Alpha (B.1.1.7), the main UK circulating variant) and 26 May to 8 July 2021 (Delta, the main UK circulating variant), with all children unvaccinated (as per national policy at the time). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (≥28 day) illness, and calculated odds ratios for symptoms presenting within the first 28 days of illness. RESULTS: 694 (276 younger (5-11 years), 418 older (12-17 years)) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2-9.75) with Alpha, 5 days (IQR 2-9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2-5) symptoms with Alpha, 4 (IQR 2-7) with Delta; in older children, 5 (IQR 3-8) symptoms with Alpha, 6 (IQR 3-9) with Delta infection ). The odds of presenting several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. CONCLUSIONS: COVID-19 in UK school-aged children due to SARS-CoV-2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden.

Strain wars 3: Differences in infectivity and pathogenicity between Delta and Omicron strains of SARS-CoV-2 can be explained by thermodynamic and kinetic parameters of binding and growth.

In this paper, for the first time, empirical formulas have been reported of the Delta and Omicron strains of SARS-CoV-2. The empirical formula of the Delta strain entire virion was found to be CH1.6383O0.2844N0.2294P0.0064S0.0042, while its nucleocapsid has the formula CH1.5692O0.3431N0.3106P0.0060S0.0043. The empirical formula of the Omicron strain entire virion was found to be CH1.6404O0.2842N0.2299P0.0064S0.0038, while its nucleocapsid has the formula CH1.5734O0.3442N0.3122P0.0060S0.0033. Based on the empirical formulas, standard thermodynamic properties of formation and growth have been calculated and reported for the Delta and Omicron strains. Moreover, standard thermodynamic properties of binding have been reported for Wild type (Hu-1), Alpha, Beta, Gamma, Delta and Omicron strains. For all the strains, binding phenomenological coefficients and antigen-receptor (SGP-ACE2) binding rates have been determined and compared, which are proportional to infectivity. The results show that the binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain. The Omicron strain is characterized by a greater infectivity, based on the epidemiological data available in the literature. The increased infectivity was explained in this paper using Gibbs energy of binding. However, no indications exist for decreased pathogenicity of the Omicron strain. Pathogenicity is proportional to the virus multiplication rate, while Gibbs energies of multiplication are very similar for the Delta and Omicron strains. Thus, multiplication rate and pathogenicity are similar for the Delta and Omicron strains. The lower number of severe cases caused by the Omicron strain can be explained by increased number of immunized people. Immunization does not influence the possibility of occurrence of infection, but influences the rate of immune response, which is much more efficient in immunized people. This leads to prevention of more severe Omicron infection cases.

Strain wars 2: Binding constants, enthalpies, entropies, Gibbs energies and rates of binding of SARS-CoV-2 variants.

SARS-CoV-2 virus is the cause of COVID-19 pandemic and belongs to RNA viruses, showing great tendency to mutate. Several dozens of mutations have been observed on the SARS-CoV-2 virus, during the last two years. Some of the mutated strains show a greater infectivity and are capable of suppressing the earlier strains, through interference. In this work, kinetic and thermodynamic properties were calculated for strains characterized by various numbers and locations of mutations. It was shown that mutations lead to changes in chemical composition, thermodynamic properties and infectivity. Through competition, the phenomenon of interference of various SARS-CoV-2 strains was explained, which results in suppression of the wild type by mutant strains. Standard Gibbs energy of binding and binding constant for the Omicron (B.1.1.529) strain were found to be ΔBG° = -45.96 kJ/mol and KB = 1.13 ∙ 10+8 M-1, respectively.

Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination.

As people around the world work to stop the COVID-19 pandemic, mutated COVID-19 (Delta strain) that are more contagious are emerging in many places. How to develop effective and reasonable plans to prevent the spread of mutated COVID-19 is an important issue. In order to simulate the transmission of mutated COVID-19 (Delta strain) in China with a certain proportion of vaccination, we selected the epidemic situation in Jiangsu Province as a case study. To solve this problem, we develop a novel epidemic model with a vaccinated population. The basic properties of the model is analyzed, and the expression of the basic reproduction number R 0 is obtained. We collect data on the Delta strain epidemic in Jiangsu Province, China from July 20, to August 5, 2021. The weighted nonlinear least square estimation method is used to fit the daily asymptomatic infected people, common infected people and severe infected people. The estimated parameter values are obtained, the approximate values of the basic reproduction number are calculated R 0 ≈ 1.378 . Through the global sensitivity analysis, we identify some parameters that have a greater impact on the prevalence of the disease. Finally, according to the evaluation results of parameter influence, we consider three control measures (vaccination, isolation and nucleic acid testing) to control the spread of the disease. The results of the study found that the optimal control measure is to dynamically adjust the three control measures to achieve the lowest number of infections at the lowest cost. The research in this paper can not only enrich theoretical research on the transmission of COVID-19, but also provide reliable control suggestions for countries and regions experiencing mutated COVID-19 epidemics.

The Runaway Evolution of SARS-CoV-2 Leading to the Highly Evolved Delta Strain.

In new epidemics after the host shift, the pathogens may experience accelerated evolution driven by novel selective pressures. When the accelerated evolution enters a positive feedback loop with the expanding epidemics, the pathogen's runaway evolution may be triggered. To test this possibility in coronavirus disease 2019 (COVID-19), we analyze the extensive databases and identify five major waves of strains, one replacing the previous one in 2020-2021. The mutations differ entirely between waves and the number of mutations continues to increase, from 3-4 to 21-31. The latest wave in the fall of 2021 is the Delta strain which accrues 31 new mutations to become highly prevalent. Interestingly, these new mutations in Delta strain emerge in multiple stages with each stage driven by 6-12 coding mutations that form a fitness group. In short, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from the oldest to the youngest wave, and from the earlier to the later stages of the Delta wave, is a process of acceleration with more and more mutations. The global increase in the viral population size (M(t), at time t) and the mutation accumulation (R(t)) may have indeed triggered the runaway evolution in late 2020, leading to the highly evolved Alpha and then Delta strain. To suppress the pandemic, it is crucial to break the positive feedback loop between M(t) and R(t), neither of which has yet to be effectively dampened by late 2021. New waves after Delta, hence, should not be surprising.

Huge Excess Mortality Due to the Delta Strain of COVID-19 in Japan in August 2021

Background: No remarkable excess mortality attributable to COVID-19 has been observed in Japan until the delta strain of COVID-19 emerged. Object: We sought to quantify high pathogenicity of the delta strain using the National Institute of Infectious Diseases (NIID) model. Method: We applied the NIID model to deaths of all causes from 1987 up through August 2021 for the whole of Japan. Results: Results in Japan show 4105 excess mortality in August 2021 in Japan. It was estimated as 3.8% of the baseline. Discussion and Conclusion: We found substantial excess mortality since the outbreak of COVID-19 had emerged in August 2021, in Japan. It might be due to spread of delta strain at that time.

Strain Wars: Competitive interactions between SARS-CoV-2 strains are explained by Gibbs energy of antigen-receptor binding.

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method

This paper presents a deep learning approach to predict new COVID-19 infected cases in a specific country with insufficient data at the onset of the outbreak. We collected data on daily new confirmed cases in several countries of the region where COVID-19 occurred earlier and caused more severe effects than in Vietnam. Then we computed some deep machine learning models to adapt the spreading speed of Delta strain in each nation to generate various scenarios for the epidemic situation in Vietnam. We used models based on recurrent neural networks (RNN) architectures such as long-short term memory (LSTM), gated recurrent unit (GRU), and several hybrid structures between LSTM and GRU. Learning from the experiments in this research, we built a set of circumstances for COVID-19 in Vietnam. We also found that GRU always gives the best performance in terms of MSE, while LSTM is the worst. © 2021, Springer Nature Singapore Pte Ltd.