Meta-analysis and comprehensive study of coronavirus outbreaks: SARS, MERS and COVID-19

Factors influencing COVID-19 vaccination intentions and the moderating effect of perceived behavioral control among pregnant women: a cross-sectional study applying the revised Theory of Planned Behavior

PURPOSE

This study explored factors that influence coronavirus disease 2019 (COVID-19) vaccination intentions during pregnancy and examined the moderating effect of perceived behavioral control based on the revised Theory of Planned Behavior.

METHODS

This cross-sectional online survey recruited 227 Korean pregnant women from an online community. Data were collected from December 2021 to January 2022 and analyzed using independent t-test, analysis of variance, Pearson correlation coefficients, and multiple regression analysis. The PROCESS macro (model 1) and simple slope analysis were used to investigate the moderating effect of perceived behavior control.

RESULTS

Attitudes (β=.44, p<.001) and subjective norms (β=.36, p<.001) were identified as factors influencing COVID-19 vaccination intentions during pregnancy. In the final regression model, the total explanatory power of the variables was 44% (F=90.47, p<.001). The moderating effect of perceived behavioral control in the relationship between attitudes and vaccination intention was not statistically significant (B=0.07, p=.382). However, it showed a significant moderating effect in the relationship between subjective norms and vaccination intentions (B=0.06, p=.046). For simple slope analysis, perceived behavioral control was classified into three levels (low, moderate, and high), and the effect of subjective norms on vaccination intention was analyzed. Pregnant women with a high level of perceived behavioral control showed the strongest relationship (b=0.45, p<.001), indicating a moderating effect of perceived behavioral control.

CONCLUSION

To increase vaccination intention among pregnant women, it is imperative to implement programs that focus on improving pregnant women's attitudes, subjective norms, and perceived behavioral control toward vaccination, with particular attention to those with low perceived behavioral control.

A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown

Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R² values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.

Adherence to infection prevention and control measures and risk of exposure among health-care workers: A cross-sectional study from the early period of COVID-19 pandemic in Addis Ababa, Ethiopia

BACKGROUND AND AIM

Healthcare workers (HCWs) are considered a high-risk group for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, ascribed to the amount of time they spend in health-care facilities (HCFs). This study aimed to assess HCWs' compliance with Infection Prevention and Control (IPC) procedures and the risk of exposure during the early period of the pandemic in Addis Ababa, Ethiopia.

METHODS

A descriptive cross-sectional survey was conducted from June to September 2020. With a response rate of 79.2%, a standardized questionnaire was administered among 247 HCWs, working in eight HCFs. Descriptive and multivariate regression analysis was carried out in STATA version 16.

RESULTS

About 22.5% (55) of the HCWs had proper adherence to IPC procedures. Of the total participants, 28.2% (69) had proper use of Personal Protective Equipment (PPE), 40% (98) had proper hand hygiene practices, and 33.1% (81) had frequently cleaned their working environment. HCWs who received training on IPC protocols were four times more likely to follow IPC standards than those with no training (adjusted odds ratio [AOR] = 3.93; 95% confidence interval [CI]: 1.46, 10.58). Besides, HCWs working in treatment centers were four times more likely to follow IPC standards than those working in conventional hospitals (AOR = 3.61; 95% CI:1.63, 8.02). Nurses were four times more likely to have adherence to IPC measures than cleaners and runners (AOR = 4.37; 95% CI: 1.38-13.88).

CONCLUSION

The nature and magnitude of the pandemic did not introduce the required degree of adherence to IPC procedures, per se does not match the level of diligence needed to halt SARS-CoV-2 transmission. Our finding suggested that providing periodic training of HCWs with particular emphasis on nonclinical staff is commendable. Furthermore, it is necessary to maintain resilent IPC in HCF through continous follow up and safety drills, to assess the readiness of HFCs' adherance to IPC measures under normal circumstances, which could improve prepardeness for an effective response during epidemics.

A metabolic blueprint of COVID-19 and long-term vaccine efficacy

Viruses are obligatory protein-coated units and often utilize the metabolic functions of the cells they infect. Viruses hijack cellular metabolic functions and cause consequences that can range from minor to devastating, as we have all witnessed during the COVID-19 pandemic. For understanding the virus-driven pathogenesis and its implications on the host, the cellular metabolism needs to be elucidated. How SARS-CoV-2 triggers metabolic functions and rewires the metabolism remains unidentified but the implications of the metabolic patterns are under investigation by several researchers. In this review, we have described the SARS-CoV-2-mediated metabolic alterations from in vitro studies to metabolic changes reported in victims of COVID-19. We have also discussed potential therapeutic targets to diminish the viral infection and suppress the inflammatory response, with respect to evidenced studies based on COVID-19 research. Finally, we aimed to explain how we could extend vaccine-induced immunity in people by targeting the immunometabolism.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Evolutionary progression of collective mutations in Omicron sub-lineages towards efficient RBD-hACE2: Allosteric communications between and within viral and human proteins

The interaction between the Spike (S) protein of SARS-CoV-2 and the human angiotensin converting enzyme 2 (hACE2) is essential for infection, and is a target for neutralizing antibodies. Consequently, selection of mutations in the S protein is expected to be driven by the impact on the interaction with hACE2 and antibody escape. Here, for the first time, we systematically characterized the collective effects of mutations in each of the Omicron sub-lineages (BA.1, BA.2, BA.3 and BA.4) on both the viral S protein receptor binding domain (RBD) and the hACE2 protein using post molecular dynamics studies and dynamic residue network (DRN) analysis. Our analysis suggested that Omicron sub-lineage mutations result in altered physicochemical properties that change conformational flexibility compared to the reference structure, and may contribute to antibody escape. We also observed changes in the hACE2 substrate binding groove in some sub-lineages. Notably, we identified unique allosteric communication paths in the reference protein complex formed by the DRN metrics betweenness centrality and eigencentrality hubs, originating from the RBD core traversing the receptor binding motif of the S protein and the N-terminal domain of the hACE2 to the active site. We showed allosteric changes in residue network paths in both the RBD and hACE2 proteins due to Omicron sub-lineage mutations. Taken together, these data suggest progressive evolution of the Omicron S protein RBD in sub-lineages towards a more efficient interaction with the hACE2 receptor which may account for the increased transmissibility of Omicron variants.

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

Introduction

The unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.

Methodology

We obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.

Results

At the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.

Discussion and Conclusion

Both analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Surgical safety in the COVID-19 era: present and future considerations

The impacts of coronavirus disease 2019 (COVID-19) have been globally paradigm shifting in all aspects. Surgeons have experienced unprecedented changes regarding operation schedules, preparations before surgery, and the precautions needed both during and after surgery. Many medical centers simultaneously reported a decrease in their numbers of surgeries, whether they were elective or emergent, or for cancerous or benign resections. However, accumulated surgical outcomes from the last 2 years of experience presented postoperative morbidity and mortality data that were comparable to the pre-pandemic era, whether in elective or urgent settings. Although COVID-19 showed a significant association with postoperative morbidity and mortality, the majority of noninfected patients could be treated successfully with stringent mitigation protocols. Initially recommended to be avoided at the start of the pandemic, minimally invasive surgery seems to be safe and feasible according to reported surgical outcomes. Numerous sets of guidelines have now been produced from medical societies and adhering to the basic precautions has been found to be practicable. It is crucial that health care systems and surgical staff remain vigilant and attentive to the ever-changing situation in this pandemic in order to provide optimal medical support to their patients.

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Financial markets across the globe have seen rapid volatility and uncertainty owing to scary and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns, curfews, emergencies, etc. has stoked the high quantum of chaotic movement in equity markets and resulted in perplexed investor behaviour. It, therefore, is of paramount practical relevance to measure predictability of market fear at such a crucial juncture of time. Market fear can effectively be measured in terms of implied and historic volatility of equity markets. The present study chooses India VIX and 20-day rolling standard deviation of NIFTY returns to account for implied and historic volatility respectively during the ongoing COVID-19 timeline. Pertinent macroeconomic constructs, technical indicators and Google search volume index on meaningful keywords have been chosen as raw explanatory features for inspecting predictability. Boruta feature selection methodology has been used in a supervised manner to select significant features. State-Of-The-Art machine and deep learning algorithms namely Gradient Boosting (GB), Extra Tree Regression (ERT), Deep Neural Network (DNN), Long Short Term Memory Network (LSTM) are then used on processed feature set to scrupulously evaluate the quantum of predictability of said assets. The integrated predictive frameworks have been subjected to a battery of numerical and statistical checks to draw inferences. Additionally, Explainable AI frameworks have been used to analyse the nature of influence of respective features. Findings indeed suggest that despite exhibiting high degree of volatile traits, both India VIX and historic volatility can be predicted utilizing the proposed architectures effectively and serve practical actionable insights.