Evolution Of COVID-19 Pandemic Research Articles

Infodemic- A new rapidly evolving virtual communicable pandemic with global threat! Hypothetical or real?

‘Infodemic’ is a pandemic of misinformation spread in a pandemic manner regarding health issues of global concern. Globally, Infodemic is rapidly rising and more adverse outcomes are observed in social, economic and family lives. Infodemic is reported first time during SARS in 2003 & now during evolution of COVID-19 pandemic in 2020. Infodemic is more rapidly spreading pandemic than any communicable disease due to its ultrafast method of transmission by means of electronic & social media in the form of print or digital platform. Here, in Infodemic, the culprit is ‘misinformation’ spread without any scientific evidence regarding health issues of global concern. During COVID-19 pandemic infodemic evolved parallel with actual natural viral disaster and documented comparable effects on mankind globally. Infodemic is considered a global issue of concern for this decade. Infodemic in COVID-19 pandemic was documented as source of agent to cause & its evolution globally, available treatment options such as Remdesivir and its safety with efficacy & lastly, COVID-19 vaccines and its role in preventing and protecting COVID-19 disease with adverse events. Infodemic needs more stringent steps to control before its evolution, which includes active involvement of health experts, medical scientists, government agencies and trust organizations working worldwide. Timely shared right and scientifically evidenced proof information is a key step in controlling misinformation and ‘infodemic’.

An overview of Brazilian working age adults vulnerability to COVID-19

Brazil is a country of continental dimensions, where many smaller countries would fit. In addition to demographic, socioeconomic, and cultural differences, hospital infrastructure and healthcare varies across all 27 federative units. Therefore, the evolution of COVID-19 pandemic did not manifest itself in a homogeneous and predictable trend across the nation. In late 2020 and early 2021, new waves of the COVID-19 outbreak have caused an unprecedented sanitary collapse in Brazil. Unlike the first COVID-19 wave, in subsequent waves, preliminary evidence has pointed to an increase in the daily reported cases among younger people being hospitalized, overloading the healthcare system. In this comprehensive retrospective cohort study, confirmed cases of hospitalization, ICU admission, IMV requirement and in-hospital death from Brazilian COVID-19 patients throughout 2020 until the beginning of 2021 were analyzed through a spatio-temporal study for patients aged 20–59 years. All Brazilian federative units had their data disaggregated in six periods of ten epidemiological weeks each. We found that there is a wide variation in the waves dynamic due to SARS-CoV-2 infection, both in the first and in subsequent outbreaks in different federative units over the analyzed periods. As a result, atypical waves can be seen in the Brazil data as a whole. The analysis showed that Brazil is experiencing a numerical explosion of hospitalizations and deaths for patients aged 20–59 years, especially in the state of São Paulo, with a similar proportion of hospitalizations for this age group but higher proportion of deaths compared to the first wave.

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of COVID-19 in the third world is really a disaster in current days, especially with the emergence of the delta variant of COVID-19 that is widespread. Required inspections should be carried out to monitor the COVID-19 spread in daily life and allow primary diagnosis of suspected cases, and long-term clinical laboratory monitoring. Healthcare professionals or radiologists can exploit AI (Artificial Intelligence) tools to quickly and reliably identify the cases of COVID-19. This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning). The objective is to perform multi-class and binary classification of the images in order to determine pneumonia and COVID-19 case. The TL is feasible, when using a small dataset by transferring knowledge from natural image classification to medical image classification. Two types of TL are used. The first type is fine-tuning of the DenseNet121, Densenet169, DenseNet201, ResNet50, ResNet152, VGG16, and VGG19 models. The second type is deep tuning of the LeNet-5, AlexNet, Inception naïve v1, and VGG16 models. Extensive tests have been carried out on datasets of chest X-ray and CT images with different training/testing ratios of 80%:20%, 70%:30%, and 60%:40%. Experimental results on 9,270 chest X-ray ray and 2,762 chest CT images acquired from different institutions show that the TL is effective with an average accuracy of 98.49%.

COVID-19 pandemic and mental health in Spain: An analysis of their relationship using Google Trends

IntroductionThis study aims to examine the public interest that exists on Internet regarding various mental health topics and its relationship with evolution of COVID-19 pandemic in Spain. Materials and methodsGoogle Trends was used to explore relative search volume (RSV) for the following terms related with mental health (TRMH): “anxiety”, “depression”, “stress”, “insomnia” and “suicide”; between January and December 2020. The cross-correlation function was performed to assess association between new COVID-19 cases and RSV levels for TRMH. Finally, Mann–Whitney test was used to examine differences between RSV values for TRMH before and after of state of alarm declarations on March and October 2020. ResultsThe “anxiety” term showed the highest RSV indices. A significant correlation was found between new COVID-19 cases and RSV for “anxiety” with a time-lag of +1 week (r=0.49; p<.05). Was found an increase of SRV for “anxiety” (U=0.00; p=.01) and a decrease of SRV for “depression” (U=1.00; p=.04) between 4-week period before and after state of alarm of March 2020. Regarding the state of alarm of October 2020, a higher RSV for “anxiety” (U=0.50; p=.02) was found in the four weeks after it compared with a similar previous period. ConclusionsAnxiety is the mental health topic of greatest public interest on Internet in context of COVID-19 pandemic. Public concern about anxiety rises one week after the increase in COVID-19 cases and is greater after introduction of control measures that entail any type of mobility restriction or activity limitation. There is a greater general need for information on anxiety at specific times in the pandemic evolution.

Evolution of COVID-19 Pandemic in India

A mathematical analysis of patterns for the evolution of COVID-19 cases is key to the development of reliable and robust predictive models potentially leading to efficient and effective governance against COVID-19. Towards this objective, we study and analyze the temporal growth pattern of COVID-19 infection and death counts in various states of India. Our analysis up to August 4, 2020, shows that several states (namely Maharashtra, Tamil Nadu, West Bengal) have reached t^2 power-law growth, while Gujarat and Madhya Pradesh exhibit linear growth. Delhi has reached sqrt{t} phase and may flatten in the coming days. However, some states have deviated from the universal pattern of the epidemic curve. Uttar Pradesh and Rajasthan show a gradual rise in the power-law regime, which is not the usual trend. Also, Bihar, Karnataka, and Kerala are exhibiting a second wave. In addition, we report that initially, the death counts show similar behavior as the infection counts. Later, however, the death growth rate declines as compared to the infection growth due to better handling of critical cases and increased immunity of the population. These observations indicate that except Delhi, most of the Indian states are far from flattening their epidemic curves.

Saliva: an important alternative for screening and monitoring of COVID-19 in children.

The increasing number of cases of COVID-19 worldwide poses challenges to healthcare systems not only in effectively identifying individuals positive for SARS-CoV-2, but also in isolating cases to minimise contagion in early diagnosing more severe cases that will need hospitalization. Less-invasive collection methods are indispensable in a pandemic scenario as large-scale tests are necessary to understand the actual evolution of contagion in different populations, thus enabling decision-making based on scientific evidence. Saliva has been shown to be an alternative for diagnosing viral infections as this biological fluid can be easily and quickly collected without using specific devices and causing less discomfort during collection, which is an important factor for use in children. Despite the smaller percentage of severe cases of COVID-19 among children, they seem to play an important role in the contagion as they have the same potential of transmission as that of adults. Knowing the evolution of COVID-19 pandemic in children is extremely important, mainly regarding the changing in rules of social distancing, such as re-opening schools and recreational activities spaces. In addition, countless cases of a severe multi-systemic inflammatory syndrome that shares clinical and laboratory features with Kawasaki's disease have been recently related to SARS-CoV-2 infections in children, adolescents and young adults. In view of this scenario, the aim of this study was to present saliva as an alternative for seeking diagnostic and prognostic markers of COVID-19 in children, including adequate sample collection techniques for different age groups.