Progression Of COVID-19 Pandemic Research Articles

Temporal variations of vaccine hesitancy amid the COVID-19 outbreaks in Hong Kong.

To inform the dynamic adjustments of vaccination campaigns, this study examined the transitions among vaccine hesitancy profiles over the COVID-19 pandemic progression and their predictors and outcomes. The transition patterns among hesitancy profiles over three periods were identified using a latent transition analysis with individuals from a longitudinal cohort study since the emergence of COVID-19 in Hong Kong. Four profiles (i.e., skeptics, apathetics, fence-sitters, and believers) emerged consistently over time. From Period 1 (third and fourth pandemic waves) to Period 2 (dormant period, vaccine rollout), 14.17% of believers became fence-sitters (ambivalization), and 12.11% of fence-sitters became apathetics (apathetization). From Period 2 to Period 3 (omicron surge and vaccine mandates), 20.21% of believers became fence-sitters. Lower trust in government predicted a transition to skepticism, whereas higher trust predicted the opposite. Staying as believers was associated with decreased hygienic and social distancing behavior. The stable hesitancy profiles amid the rapid vaccine uptake suggest that structural factors rather than personal agency may drive the surge. Ambivalization and apathetization may signal disengagement in preventive behaviors. Trust in the government is crucial in the pandemic response. Public health interventions may improve compliance with guidelines and prevent skepticism and apathy.

Circulating Variants of SARS-CoV-2 Among Macedonian COVID - 19 Patients in the First Year of Pandemic.

Genomic epidemiology has proven to be a useful tool for investigating pandemic outbreaks and tracking pathogen spread and evolution. This study describes the circulation of SARS-CoV-2 strains in N. Macedonia during a period of one year, encompassing three waves of the COVID-19 pandemic. A certain percentage (2-3%) of positive cases were continuously selected and analyzed by whole genome sequencing (WGS) technology. Using this approach, a total of 337 SARS-CoV-2 genomes were sequenced and 26 different lineages belonging to 7 clades were detected. During the first wave of the pandemic, the most dominant lineage was B.1.1, followed by B.1.1.70, which became the most dominant in the second wave. The B.1.1.7 lineage completely overpassed all other variants in the third wave. Our study strengthens the notion that the progression of COVID-19 pandemic is associated with emergence of new SARS-CoV-2 variants with increased virulence. The measure of the impact of this viral dynamic on the spread of the pandemic should be evaluated in association with other factors that might influence the transmission.

Impact of a Conformité Européenne (CE) Certification-Marked Medical Software Sensor on COVID-19 Pandemic Progression Prediction: Register-Based Study Using Machine Learning Methods.

BackgroundTo address the current COVID-19 and any future pandemic, we need robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of viral spread, and helps to predict the needs and timing of professional health care.ObjectiveThe objective of this study was to use a Conformité Européenne (CE)-marked medical online symptom checker service, Omaolo, and validate the data against the national demand for COVID-19–related care to predict the pandemic progression in Finland.MethodsOur data comprised real-time Omaolo COVID-19 symptom checker responses (414,477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare from March 16 to June 15, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and extreme gradient boosting (XGBoost) models together with F-score and mutual information feature preselectors to predict the admissions once a week, 1 week in advance.ResultsOur models reached a mean absolute percentage error between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature preselector.ConclusionsAccurate short-term predictions of COVID-19 patient admissions can be made, and both symptom check questionnaires and daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic.

Effect of Ivermectin mass drug administration on the COVID-19 Pandemic

Uttarakhand, a hilly state of India adopted the strategy of mass drug administration of ivermectin during the second wave of COVID-19 pandemic as a measure to control the peak due to failing health infrastructure and health facility with sudden cases surge. This study was hereby planned with an aim of analyzing the effect of mass drug administration of ivermectin on the COVID-19 pandemic progression in the State of Uttarakhand. The trend of pandemic progression in five states of India was studied taking case growth rate, active case rate and case fatality rate as indicators. The percentage change in the indicators with the progression of pandemic was calculated for all five states and compared. The projected increased number of deaths, the state of Uttarakhand would have experienced, if the state had the declining average case growth of the other states taken for study was also calculated. The data analysis shows that the Uttarakhand had a sharp fall in the active cases by 3 to 4 weeks post lockdown, the period corresponding with maximum coverage of ivermectin distribution in the state.

Progression of COVID-19 Pandemic in India: A Linear Functional Concurrent Regression Analysis Approach

Background: COVID-19 is a disasterous pandemic that the world has ever faced. It is affecting the global health system irrespective of race, ethnicity, environment, and economic status. This study is conducted with the aim of assessing the progression of the COVID-19 pandemic in India. Methods: This article uses the functional concurrent regression analysis approach to describe the pattern of daily reported confirmed cases of COVID-19 in India. The approach provides an excellent fit to the daily reported confirmed cases of the disease. The data used in this study have been taken from covid19india.org. Results: Estimated value of the parameter kbof the model is highly volatile. During the first phase of the pandemic which last up to 31st March 2020, value was very high. During 31st March to 19th July 2020 except for a few exceptions. Its value again increased rapidly from 17th February 2021 to 16th April 2021 and started decreasing after mid-March, 2021 and continued decreasing till present. Conclusion: The data-driven approach used in this study is purely empirical and does not make any assumption about the progression of the pandemic or about the data. The article suggests that based on the parameter of the model, an early warning system may be developed and institutionalised to undertake the necessary measures to control the spread of the disease, thereby controlling the pandemic.

Mutual effect of labour market development and COVID-19 pandemic progression

This paper tackles the economic impacts of COVID-19 pandemic on the labor markets and human capital. Specifically, it looks into the issues the pandemic brought upon the human resources and personnel during coronavirus lockdowns. The high level of globalization characteristic of the modern economy has only exacerbated the negative impact of the pandemic. At the moment, it remains impossible to assess the qualitative and quantitative parameters of the damage caused to the economies of countries and regions by this event. However, it is possible to identify the main directions of analysis of the consequences of the pandemic, including in terms of the impact on the state of the labor market, which was the main goal of this study. In particular, the authors highlight a number of consequences, the work on overcoming which is still to be done by the Russian socio-economic system. The most obvious of them is the growth of unemployment, the release of part of the employed and the reduction in the number of jobs, and this is observed extremely unevenly across the sectors of the economy. Nevertheless, the authors point out that the problem of staff release is aggravated by the size of the share of informal employment in the labor market, especially in the sectors of public catering, leisure and tourism that have been most affected by the pandemic. As the second important problem, the authors point to ineffective staff reduction, the dismissal of useful and valuable employees of some organizations while maintaining an unnecessarily bloated staff of others. It also mentions the reasons and forms of staff retention, some of which, being either forced or economically and technologically attractive, give rise to additional problems. The authors call an important social consequence of the pandemic a reduction in the number and level of personal contacts in society, in particular, in working groups. As a result, the author's vision of the long-term consequences of current events for the state of the labor market, employment of the population and the economy as a whole is proposed.)

The impact of social media on young web users’ psychological well-being during the COVID-19 pandemic progression

Aim. The present study empirically investigates and theoretically substantiates the results of the impact of social media on young web-users’ psychological well-being during the forced self-isolation caused by the progression of the COVID-19 pandemic (N = 254). Materials and methods. Standardized valid psycho-diagnostic methods, the author’s questionnaire (A. Hudimova, 2021), correlation and factor analyses were used to identify young web users’ patterns of social media involvement during the forced self-isolation. Results. The results show that during the global COVID-19 pandemic, young web users give preference for passive social media use rather than for communication. The obtained results showed an expansion in the time spent via social media by young web users. It was found that the progression of the COVID-19 pandemic is accompanied by the participants’ experience of negative emotions and fears of the unknown (r = .204; p <.01). It is substantiated that increasing immersion of young web users in social media is a kind of strategy to escape from bad thoughts (r = .271; p <.01). Significantly, it is stated that uncontrolled use of social media causes sleep disorders during isolation (r = .444; p <.01). Conclusions. The study proves that young people spend almost all day online due to the obsessive pattern of social media involvement and/or procrastination, which often provokes withdrawal syndrome upon the attempt to distract from them. The lack of controlled time spending on social media during self-isolation provokes an exacerbation of anxiety, apathy, depressed mood, and a sense of isolation from social reality. The obtained results provide evidence that the causal relations of passive social media use provoke an exacerbation of feelings of alienation, disrupt the healthy rhythm of sleep, and psychological state of young web-users during the progression of the COVID-19 pandemic.

Bioinformatic Algorithm and Prediction to Control SARS-Cov-2 Spike Protein Variant on COVID-19 Pandemic Progression

Recently severe SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global COVID-19 pandemic. Systemic tracking algorithms of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographical levels, the shift occurred even in narrow scope of local epidemics. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have fitness advantage. Based on findings that the G614 variant grows to a higher titer as pseudo-typed virions. These findings illuminate changes important for mechanistic understanding of the virus and support continuing surveillance of Spike mutations to aid with development of immunological interventions. Furthermore, predictive stewardship would be conducted for protection and limitation of transmissibility.

Effects of Demographic and Weather Parameters on COVID-19 Basic Reproduction Number

It is hard to overstate the importance of a timely prediction of the COVID-19 pandemic progression. Yet, this is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies (which are highly sensitive to the testing capacity, levels of introduced social distancing measures, etc.), and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive numberR0(i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naïve population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of potentially interesting demographics and weather parameters for these countries (where data was available), and seek their correlations with the rate of COVID-19 spread. While some of the already reported or assumed tendencies (e.g., negative correlation of transmissibility with temperature and humidity, significant correlation with UV, generally positive correlation with pollution levels) are also confirmed by our analysis, we report a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but absence of correlation with population densityper se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels. Detailed comparisons of obtained results with previous findings, and limitations of our approach, are also provided.

The Risk Distribution of COVID-19 in Indonesia: A Spatial Analysis.

The COVID-19 pandemic in Indonesia started with 2 cases on March 2, 2020, and as of May 11, a total of 14 265 people were infected. The government through Task Force for COVID-19 Rapid Response informs the progress of COVID-19 pandemic in Indonesia, but no one has provided a picture of the risk distribution in all provinces in Indonesia. This research is intended to identify high-risk provinces based on risk factors in each province and to find COVID-19 hotspots. This is an ecological study that used aggregate data. We used a map to present the risk distribution in Indonesia, and Local Indicators of Spatial Association (LISA) to define the hotspot area of COVID-19 in Indonesia. There are 6 provinces identified as high-risk areas of COVID-19 in Indonesia, and the hotspot provinces are Banten, DKI Jakarta, West Java, East Java, and Central Java.

Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide

Error analysis and data visualization of positive COVID-19 cases in 27 countries have been performed up to August 8, 2020. This survey generally observes a progression from early exponential growth transitioning to an intermediate power-law growth phase, as recently suggested by Ziff and Ziff. The occurrence of logistic growth after the power-law phase with lockdowns or social distancing may be described as an effect of avoidance. A visualization of the power-law growth exponent over short time windows is qualitatively similar to the Bhatia visualization for pandemic progression. Visualizations like these can indicate the onset of second waves and may influence social policy.