COVID-19 Positive Cases Research Articles

Mobile phone data reveals spatiotemporal recreational patterns in conservation areas during the COVID pandemic

Understanding visitation patterns is crucial in developing effective conservation strategies for protected areas, as it serves as an indicator for operating an ecosystem management plan that balances biodiversity and ecosystem services intertwined with public health and social benefits. However, limited data availability during the COVID-19 pandemic has hindered the comprehensive understanding of temporal changes in realized cultural ecosystem services, particularly in recreational activities within these areas. Our study utilized GPS data from mobile phones to quantify visitor characteristics and their contribution to recreational ecosystem services in protected areas at a national scale during the COVID-19 pandemic. We estimated the pandemic's relative impact on visitor patterns at 98 visitor centers in national parks and Ramsar sites in Japan. The total number of visitors and travel distance in various sizes of protected areas decreased after the outbreak of COVID-19. The number of visitors in the protected areas displayed a quick recovery despite the increasing positive COVID-19 cases during the following summer. Post-pandemic, visitors showed a preference for less densely populated protected areas closer to their home range. Our findings partly suggest that protecting a diverse range of conservation areas along the urban gradient could be an effective strategy for maintaining the resilience of recreational services during a prolonged pandemic.

Factors associated with child marriage during the COVID-19 pandemic in Indonesia

AbstractThe aim of this study is to investigate factors associated with the applications of child marriage dispensation submitted to the Religious Courts during the COVID-19 pandemic in Indonesia, particularly in 2019–2021. The number of child marriage dispensation cases was analyzed together with other socioeconomic variables, including poverty and jobless rates, junior and senior high school completion rates, number of cerai talak (a divorce initiated by the husband) and cerai gugat (a divorce initiated by the wife) applications, number of COVID-19 positive cases, and number of deaths by COVID-19. The differences between 2021 and 2019 were analyzed using the paired t-test and repeated measures analysis of variance (ANOVA). The relationship between variables was analyzed using the Pearson product-moment correlation coefficient. Hierarchical multiple regression with the entering method was used to determine the predictors of the number of child marriage dispensation cases in the years 2019, 2020, and 2021 and the change from 2019 to 2021. New dispensation underage marriage applications in 2019, 2020, and 2021 were 24,457 (M =843, SD =1519), 63,391 (M = 2186, SD = 4085), and 62,167 (M = 2144, SD = 4058), which were significantly different (F(1.0, 28.6) = 7.63, p = .01) and positively correlated with poverty rate, jobless rate, cerai talak, cerai gugat, COVID-19 positive cases, and COVID-19 death cases. The multiple regression analysis for the change of dispensation underage marriage applications in the pandemic and before (years 2021–2019) was statistically significant, (F(6,22) = 117.62, p < .001), which the final model was able to account for 97% of the variance in new dispensation underage marriage applications. Collaborative multi-sector works are needed to decrease the skyrocketing child marriage numbers, especially in a crisis situation like the COVID-19 pandemic.

Assessing the relationship between organ function test results and COVID-19 severity

A number of recent reports have indicated the association of COVID-19 with multiple organ failure and the need to clarify the relationship between organ testing parameters and disease progression. Therefore, this study aimed to determine the degree of abnormal organ function test parameters and its correlation with COVID-19 severity. A cross-sectional study was conducted among COVID-19 patients admitted at Sanaka hospital, India, from August to September 2020. A total of 100 qRT-PCR-confirmed COVID-19 patients divided into groups with mild or severe cases were enrolled. The data from venous blood samples for liver, renal, cardiac and inflammatory test parameters were included from the Sanaka hospital laboratory database. Biochemical prognostic tests were carried out using a clinical automated ERBA analyzer, cardiac markers were estimated with Enzyme Linked Fluorescent Assay. The Pearson correlation analysis was used to analyze the data. Aspartate/Alanine aminotransferases and alkaline phosphatase activity, creatinine, urea and troponin levels were higher in the confirmed positive cases of COVID-19. Significantly higher levels of troponin, D-dimer and C reactive protein (CRP) were found in patients with severe COVID form compared to a mild one. A strong positive correlation between elevated D-dimer and Ferritin with CRP level was revealed in this group of patients. It was concluded that the positive relationship between serum D-dimer, ferritin levels and CRP level in patients can be considered a stable indicator of disease severity. Keywords: COVID-19, CRP, D-dimer, ferritin, liver function test, renal function test

Trends in Heparin-Induced Thrombocytopenia in the Pre-Pandemic and Peak-Pandemic Era, and Impact of COVID-19 on Mortality; An Analysis Via the National Inpatient Sample

Introduction: The recent pandemic of COVID-19 caused a significant upheaval in access to healthcare and resources across the world. The COVID-19 infection has also been associated with a hypercoagulable state that can alter multiple coagulation pathways and cause various cerebrovascular and cardiovascular insults, thus requiring the need for heparin products. While a recent study found a decreasing trend of Heparin-induced thrombocytopenia(HIT) between 2011-2014, there have yet to be any recent studies investigating the pandemic's role on HIT trends and the overall effect of COVID-19 on the mortality of these patients. We, therefore, used the National Inpatient Sample (NIS) to assess the impact of the pandemic on HIT. Methods: The 2016-2020 NIS, a set of yearly discharge records released in de-identified form with various patient characteristics, discharge and procedural codes, was used for our study. We included cases with a diagnosis for HIT via the International Classification of Diseases, Tenth Revision (ICD-10) code “D7582”. Patients with COVID-19 and other comorbidities that can influence the outcomes were also identified via their respective ICD-10 codes from past studies. The incidence of HIT per 100,000 hospitalizations between 2016-2020 was compared and linear-by-linear analysis was used to assess trends, as in previous studies. We further proceeded to compare the trends in mortality rates among HIT patients. Finally, the odds of mortality among HIT patients with and without COVID-19, restricting the sample to HIT patients from 2020, were estimated via multivariable regression models. SPSS 29.0 was used for our analyses, and a p-value <0.05 was set for statistical significance. Results: Our study included a total of 72930 cases of HIT between January 1st 2016- December 31st 2020, with 16780 cases in 2016, 16450 cases in 2017, 14590 cases in 2018, 12980 cases in 2019, and 12130 cases in 2020. The overall adjusted incidence of HIT dropped between 2016-2019 from 55.6 cases per 100,000 (in 2016) hospitalizations to 43.0 cases per 100,000 hospitalizations (in 2019). However, a rise was seen in 2020 with 43.9 cases per 100,000 hospitalizations (ptrend<0.01). On average, HIT was reported in 49.1 cases per 100,000 adult hospitalizations. However, in 2020, among the 12130 cases of HIT, there were 1745(14.4%) COVID-19 positive patients. The adjusted incidence of HIT in 2020 without COVID-19 cases of HIT was 37.6 cases per 100,000 hospitalizations. The adjusted analysis (excluding COVID-19 positive HIT cases), showed a continuous decrease in incidence of HIT between 2016-2020 (Figure 1a). Meanwhile, the mortality rates among HIT patients were 10.2% in 2016, 11.9% in 2017, 11.0% in 2018, 10.5% in 2019 and 16.2% in 2020 (ptrend<0.01). However, in 2020 the mortality rate among non-COVID-19 positive HIT patients was 10.4%, with no statistical differences between the years (ptrend=0.440) (Figure 1b). Finally, among the HIT cases of 2020, concurrent presence of COVID-19 was linked with a higher odds of mortality (50.4% vs. 10.4%, aOR 8.471, 95% CI 7.405-9.690, p<0.01). Conclusion: Our study provides a fresh analysis of the burden of the COVID-19 pandemic on the incidence and outcomes of HIT in the United States. While a decreasing incidence of HIT was seen between 2016-2020 for cases without COVID-19, in 2020, patients with the virus contributed to a rise in the overall incidence and mortality rates. COVID-19 infection has a massive impact on coagulation profiles, and while our data covers the pre-vaccination period, we strongly advocate for research via future NIS databases to better understand the progress of the pandemic on HIT in 2021 and onwards.

PULMONARY REHABILITATION IN POST COVID SEQUELAE IN INDIA

An outbreak of pneumonia was reported in Wuhan, Hubei Province, China in December 2019 and quickly spread to all Chinese provinces. 1, 2 The coronavirus disease (COVID-19) pandemic spread through 190 countries within 20 weeks from Wuhan, China, affecting 334,000 populations. This caused more than 14,500 deaths by mid-March 2020. The number of infected people has risen to 1,210,956 in the month of April 6, 2020, with 67,594 associated expirations. 4 India recorded its first COVID-19 infection case on January 30, 2020 in a student from China's capital, Wuhan. In 2020, India reported 360 positive COVID-19 cases from 23 states across the country. 4 in India, the number of asymptomatic cases was higher. On April 4, 2019, a novel coronavirus pathogen was proved to be a novel betacoronavirus. 2 Sub-micron sized viruses, narrated as "organisms at the edge of life", are liable for some of the worst human pandemics in history, like influenza (1918), smallpox (1978), and COVID-19, as of September 17, 2020. 2 The SARS-CoV-2 virus is causative for COVID-19. Nearly 29 million people have been affected by this virus, which caused 936,521 deaths worldwide (WHO, 2020). 2 Corona viruses are single-stranded positive-strand RNA viruses that are known to contain some of the largest viral genomes, up to around 32 kbp in length. Following an augmentation in the number of coronavirus genome sequences available and following efforts to investigate the diversity in the wild, the family Corona viridae now contains four genera. The structure of the coronavirus as given in figure 1 Those species that belong to the genera Alpha corona virus and Beta corona virus can infect mammalian hosts, while those of Gamma corona virus and the recently defined Delta corona virus mainly infect avian species. 3 The highly pathogenic human coronaviruses, Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), belong to lineage B (sub-genus Sarbecovirus) and lineage C (sub-genus Merbecovirus) of Betacoronavirus. 3

A reliable stochastic computational procedure to solve the mathematical robotic model

The current work presents the numerical solutions of the robotic system in the process of coronavirus. The stochastic performances using the modeling of Gudermannian neural networks (GDMNNs) are provided along with the global search genetic algorithm (GA) and rapid interior-point scheme (IPS), i.e., GDMNNs-GAIPS. An error function using the differential form of the model is created and then optimized by applying the hybridization of GA-IPS. The correctness and accuracy of the stochastic procedure GDMNNs-GAIPS is examined by using the comparison of the proposed and reference results. The reliability and substantiation of the proposed GDMNNs-GAIPS is authenticated by using the statistical operators based on the mean square error, Theil inequality coefficient and variance account for. Forty numbers of independent trials along with ten numbers of hidden neurons have been used to solve the mathematical model of robotic system to detect the positive cases of COVID-19.

Community Knowledge and Stigma About COVID-19: An Indonesian Perspective

Corona Virus Disease (COVID-19) is a new infectious disease caused by a type of coronavirus. The high number of positive COVID-19 cases in Indonesia has created a negative stigma against people who are exposed to it, not even positive patients who get negative stigma. Knowledge is the community's way of understanding COVID-19 and knowing how dangerous the disease is by assessing every aspect of prevention and transmission by knowing and understanding the COVID-19 disease. This study aims to determine the relationship between knowledge and stigma in society about COVID-19, so that efforts to prevent the occurrence of social stigma related to COVID-19 that are sustainable in society can be carried out. The research method uses a quantitative design with a cross-sectional approach. The sampling technique used purposive sampling with 170 respondents. The results showed that the public's knowledge about COVID-19 consisted of 88 people (51.8%) of whom the majority had sufficient knowledge. Meanwhile, community stigma about COVID-19 was found 122 people (71.8%) did not have stigma. The results of the correlation test found that the p-value was 0.441 > 0.05, meaning that there was no significant relationship between knowledge and public stigma about COVID-19. It is not enough just to know about COVID-19 to minimize stigma in the community, other factors need to be investigated.

Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study.

The COVID-19 pandemic caused by SARS-CoV-2 is causing ongoing human and socioeconomic losses. To know how far the virus has spread in Niger State, Nigeria, a pilot study was carried out to determine the SARS-CoV-2 seroprevalence, patterns, dynamics, and risk factors in the state. A cross-sectional study design and clustered, stratified random sampling strategy were used to select 185 test participants across the state. SARS-CoV-2 IgG and IgM rapid test kits (colloidal gold immunochromatography lateral flow system) were used to determine the presence or absence of antibodies to the virus in the blood of sampled participants across Niger State from June 26 to 30, 2020. The test kits were validated using the blood samples of some of the Nigeria Center for Disease Control-confirmed positive and negative COVID-19 cases in the state. SARS-CoV-2 IgG and IgM test results were entered into the Epi Info questionnaire administered simultaneously with each test. Epi Info was then used to calculate the arithmetic mean and percentage, odds ratio, χ2 statistic, and regression at a 95% CI of the data generated. The seroprevalence of SARS-CoV-2 in Niger State was found to be 25.4% (47/185) and 2.2% (4/185) for the positive IgG and IgM results, respectively. Seroprevalence among age groups, genders, and occupations varied widely. The COVID-19 asymptomatic rate in the state was found to be 46.8% (22/47). The risk analyses showed that the chances of infection are almost the same for both urban and rural dwellers in the state. However, health care workers, those who experienced flulike symptoms, and those who had contact with a person who traveled out of Nigeria in the last 6 months (February to June 2020) were at double the risk of being infected with the virus. More than half (101/185, 54.6%) of the participants in this study did not practice social distancing at any time since the pandemic started. Participants' knowledge, attitudes, and practices regarding COVID-19 are also discussed. The observed Niger State SARS-CoV-2 seroprevalence and infection patterns meansuggest that the virus has widely spread, far more SARS-CoV-2 infections have occurred than the reported cases, and there is a high asymptomatic COVID-19 rate across the state.

Association of HLA-DRB1*11 and HLA-DRB1*12 gene polymorphism with COVID-19 in Burkina Faso

BackgroundThe clinical manifestations of coronavirus disease (COVID-19) can vary widely, ranging from asymptomatic to severe, and may be influenced by the host genetic background. The aim of the present study was to determine the frequencies of HLA-DRB1*11 and HLA-DRB1*12 allele polymorphisms and their associations with COVID-19.MethodsIn this cross-sectional study, 198 subjects were enrolled, including 150 COVID-19 positive cases and 48 subjects who tested negative for COVID-19. Participants were recruited from the emergency, intensive care, and infectious diseases departments of the Bogodogo Centre University Hospital (CHU-B) or the routine laboratory of Centre de Recherche Biomoléculaire Pietro Annigoni (CERBA). Genomic DNA was extracted from nasopharyngeal swabs samples and multiplex PCR-SSP was used to detect the HLA-DRB1*11 and HLA-DRB1*12 alleles. The study was approved by CERS (№ 2021-02-033).ResultsThe positive cases were categorized into 38 asymptomatic (CC+), 60 symptomatic (NC+), and 52 severe cases (SC+). Females were more frequent in the overall study population (53.0%, 105/198) as well as in the negative group’s CC- (68.75%, 33/48) and SC+ (57.69%, 30/52 negative groups, whereas males were more frequent in the CC+ (63.16%, 24/38) and NC+ (53.33%, 32/60) groups. The highest mean age was observed in the SC + group. A frequency of 19.19% (38/198) and 14.65% (29/198) was found for the HLA-DRB1*11 and HLA-DRB1*12 alleles, respectively. Individuals carrying the HLA-DRB1*11 allele had an approximately sixfold higher risk of asymptomatic SARS-CoV-2 infection (OR = 5.72 [1.683–19.442], p = 0.005) based on the association analysis.ConclusionsAltogether, the present study reports high frequency of HLA-DRB1*11 and HLA-DRB1*12 alleles within a population from Ouagadougou, Burkina Faso. The results suggest that individuals carrying the HLA-DRB1*11 allele are more susceptible to COVID-19 infection but may not display symptoms.

COVID-19 Detection using Hybrid CNN-RNN Architecture with Transfer Learning from X-Rays.

Millions of people have been infected with COVID-19, which has spread quickly worldwide since the start of 2020, resulting in numerous fatalities. Identification of infected individuals is essential to control the spread of the virus. In this study, we propose a hybrid architecture that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) and leverages transfer learning to enhance the accuracy of COVID-19 detection from X-ray images. The proposed work utilizes 4 pre-trained CNN architectures, namely, InceptionnetV3, Densenet121, Inception-ResNet V2, and VGG19, to extract high-level features from the input X-ray images. These features are then fed into the second component, an RNN-based network, which captures the temporal dependencies within the extracted features. To evaluate the performance of the proposed architecture, a comprehensive dataset consisting of X-ray images from COVID-19 positive cases, non-COVID-19 pneumonia cases, and healthy individuals is used. Gradient class activation map (Grad-CAM) analysis has been applied to the obtained results to provide heat-map pictures specific to each class and coloured visualizations of the COVID-19-infected areas in CXR images. Experimental results demonstrate that the proposed hybrid CNN-RNN architecture achieves promising results in COVID-19 detection from X-ray images. The model exhibits high accuracy, precision, recall, area under the receiver operating characteristics (ROC) curve (AUC), and F1-score, outperforming other state-of-the-art methods. The combination of CNNs and RNNs enables the model to effectively capture spatial and temporal information, leading to improved performance in COVID-19 detection. The proposed hybrid architecture with transfer learning from X-ray images provides a robust and efficient solution for COVID-19 detection. The model can potentially assist healthcare professionals in making accurate and timely diagnoses, thereby contributing to the global efforts to combat the COVID-19 pandemic. In the present work, VGG19-RNN architecture outperformed all other networks in terms of accuracy. The most effective training and validation accuracy for the VGG19-RNN architecture is 99% & 97.70%, respectively, and the loss was 0.02 & 0.09 at epoch 100.

Clinical characteristics and predictors of COVID-19 in a community sample from urban areas of Chennai, Southern India.

In early 2020, with the arrival of the first case of COVID-19 in India, the laboratory at The Tamil Nadu Dr. MGR Medical University was recognized as a national testing site and received samples from two zones of Greater Chennai Corporation. This descriptive study analyzed the testing outcomes, along with demographics, self-reported symptoms, potential exposure to COVID-19 that were recorded in Sample Report Forms between June and September 2020. Nasopharygeal/oropharyngeal swabs were tested for SARS CoV-2 by RT-PCR Test. Of the 18,082 samples that were tested, 18% (3267) received a COVID-19 positive RT-PCR result for COVID-19. Among the COVID-19 positives, 40.9% (1336) were females (p < 0.000). Individuals in the age groups 21-30 and 31-40years included the largest number (767, 23.48% each) of COVID-19 positive cases. The largest number of cases were from those who were classified as Severe Acute Respiratory Illness (SARI) or Influenza Like Illness (ILI) (1657, 50.7%) followed by those who reported direct contact with a COVID-19 lab confirmed case (1163, 35.6%). Among those with symptoms, 1791 (28.9%) tested COVID-19 positive compared to 1476 who were COVID-19 positive and asymptomatic (p < 0.001). Main symptoms were cough (32.1%), fever (48.3%), body ache (21.04%) and sore throat (30.60%). In multivariable analysis, history of SARI/ILI, contact with a confirmed case and having fever, cough and sore throat were the main predictors of being COVID-19 positive. Therefore, clinical and demographic characteristics correlated with COVID-19 positivity. And classification of patients based on self-reported symptoms and risk categories offers a model to prioritize testing and vaccination efforts.

The Effect of Covid-19 on Voters in the Medan Tuntungan District Election in 2020

The Covid Sickness (Coronavirus) flare-up that happens in all nations in this present reality is progressively widespread. This instance of pneumonia of obscure etiology or Coronavirus was first recognized in Wuhan City, Hubei Area, China on December 31, 2019. Medan City is one of the regions that held regional elections for the mayoral election, In the 2020 Medan city election, Medan was included in the red zone area where one of the areas with a positive number of covid-19 reached 4,261 people with this number Medan city accounting for 75 percent of corona virus cases in North Sumatra. But in fact, with the large number of positive Covid-19 cases in the city of Medan, there is no decrease in public enthusiasm to come to the Polling Station (TPS) compared to previous elections in Medan. This study uses a type of quantitative research because this study seeks to analyze whether there is an influence of the COVID-19 pandemic on voter participation in Medan Tuntungan sub-district. This type of research is a type of descriptive research. Influence of Covid-19 on Voters in the Medan Tuntungan District Election in 2020 is as follows: Voter participation in Medan Tuntungan District is also influenced by other variables outside the Covid-19 pandemic variable, which is 83.8%, so it is appropriate if research related to voter participation is carried out again using other variables outside the Covid-19 pandemic variable.

COVID-19 Vaccination Services in the Region Kubu Raya District Border

The high spread of COVID-19 in Indonesia has impacted various aspects of life, both social, cultural, and economic. To tackle the spread of COVID-19, the government has created a vaccination program to increase immunity against diseases due to COVID-19. The survey results by the Ministry of Health of the Republic of Indonesia show that even though it is free, people who volunteer to be vaccinated against COVID-19 still need to meet expectations. Nationally, it is only 65%, while West Kalimantan is only 63%. In Kubu Raya Regency, up to August 2021, positive cases of COVID-19 reached 3,336 points; 71.82% of people who had received vaccine I got 71.82% and vaccine II only 47.67%. Service quality is one of the factors that can determine the success of the vaccination program. This research aims to define community expectations regarding the quality of COVID-19 vaccination services by Community Health Centers in the border areas of Kubu Raya Regency. The research method is quantitative, and data analysis uses the average method and Cartesian diagrams. The research results show that the COVID-19 vaccination service in Kubu Raya Regency uses a pick-up and drop-off model, meaning that the service needs to be carried out at the Community Health Center. Still, officers go to the community with elementary requirements. The COVID-19 vaccination service is carried out at the Village Hall. Officer services still need to be entirely in line with community expectations. The public expects excellent service quality; the quality of COVID-19 vaccination services is still in the good category.

A Comparative Analysis of the Incidence, Severity and Duration of Smell and Taste Loss in COVID-19 Cases Versus Non-COVID-19 Cases: A Longitudinal Cohort Study.

The COVID-19 pandemic has highlighted the relevance of olfactory and gustatory disorders. However, these symptoms can also be caused by various other factors. In this study we aimed to compare the incidence, severity and duration between COVID-19 related and non-COVID-19 related smell and taste disorders. We conducted a longitudinal cohort study using data from the Dutch biobank Lifelines, which includes over 167,000 participants. The data were collected using 27 questionnaires distributed between March 2020 and May 2022. Descriptive data and the incidence of smell and taste loss in both groups were calculated. To visualize the proportion of severity rates of symptoms, a heatmap was created. A survival analysis was conducted and presented in a reversed Kaplan-Meier curve to show the probability of having persistent smell loss in both groups. The study included 235,722 participants. The incidence of smell loss was higher in the COVID-19 positive group, when compared to the COVID-19 negative group. We found varying degrees of symptom severity in COVID-19 positive cases, ranging from mild to severe, while non-COVID-19 related cases mostly reported mild symptoms. The survival outcome for smell and taste loss was 0.12 (SE 0.03, 95% CI 0.07-0.21) in COVID-19 related cases, and was 0.17 (SE 0.03, 95% CI 0.12-0.24) in cases related to other causes. This study reveals a higher incidence and severity of smell and taste loss in individuals with COVID-19 compared to non-COVID-19 related cases. However, non-COVID-19 related smell and taste loss tend to have a longer duration.

Bayesian inference on sparse multinomial data using smoothed Dirichlet distribution with an application to COVID-19 data

We develop a Bayesian approach for estimating multinomial cell probabilities using a smoothed Dirichlet prior. The most important feature of the smoothed Dirichlet prior is that it forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet prior. We propose a shrinkage-type estimator using this Bayesian approach to estimate multinomial cell probabilities. The proposed estimator allows us to borrow information across other multinomial populations and cell categories simultaneously to improve the estimation of cell probabilities, especially in a context of sparsity with ordered categories. We demonstrate the proposed approach using COVID-19 data and estimate the distribution of positive COVID-19 cases across age groups for Canadian health regions. Our approach allows improved estimation in smaller health regions where few cases have been observed.

A-307 Spread Dynamic of SARS-CoV-2 Variants and Related Alterations in Transmission Rates: A National Scale Study

Abstract Background In late 2019, the world was surprised by the new coronavirus (SARS-CoV-2), responsible for the COVID-19 pandemic and the ability to develop and share knowledge about SARS-CoV-2 was essential in combat of the pandemic. Genomic surveillance has played a fundamental role in identifying new mutations, detecting emerging variants, and monitoring viral evolution. Although next-generation sequencing (NGS) remains the gold standard for the identification of new variants, this technology is still restricted due to the high cost. Thus, the use of accessible methods for SARS-CoV-2 monitoring is necessary. This study aimed to collect and share monitoring data of COVID-19 in Brazil and to evaluate the dynamics of SARS-CoV-2 variants during the two periods of changes in the epidemiological scenario (second and third pandemic waves) and the transmissibility rates among circulating VOCs. Methods The study evaluated 27 003 samples from all federative units in Brazil between April/2021 and January/2022. Specific primers and probes were used for the mutations K417T/N, L452R, E484K/Q, N501Y, P681R (TaqMan SARS-CoV-2 Mutation Panel), and the SGTF (S-gene target failure) by the TaqPath COVID-19 CE-IVD RT-PCR Kit according to the frequencies of the Gamma, Delta, and Omicron VOCs in the evaluated time. Samples showing an unexpected mutation profile in RT-qPCR genotyping were submitted to NGS. Data for viral targets of about 227 661 positive samples were collected to assess the period of predominance of each VOC as an inference of transmissibility potential. Results Samples were collected in all 27 Brazilian federative units. The replacement of the Gamma by Delta variant occurred in about 5 months, between June and September 2021, when the Delta variant reached 100% of infections. The transition of predominance among the Delta to Omicron variant was only 2 months, demonstrated by the progressive increase of Omicron in November (3.4%), December (67.5%), and January (97%), accompanied by the consequent growth of COVID-19 positive cases in the country. These results reinforce the higher transmissibility of Omicron compared to other variants identified in Brazil. The adopted strategy was able to identify the Gamma, Delta, and Omicron VOCs as well as the presence of other variants in 99.2% of the samples. Only 0.8% of the samples were not characterized and it was submitted to NGS. Conclusion We described a methodology that was able to effectively follow the fast establishment of Omicron VOC related to its higher transmission rates and genomic mutations that explain the highest peak of COVID-19 in our country. This study comprises the largest national-scale of genomic surveillance of SARS-CoV-2, providing information about the virus spread in Brazil.

Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023.

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed.

High altitudes, population density, and poverty: Unraveling the complexities of COVID-19 in Peru during the years 2020–2022

BackgroundSeveral factors related to hospitalizations, morbidity, and mortality from COVID-19 have been identified. However, limited exploration has been done on geographic and socioeconomic factors that could significantly impact these outcomes. ObjectivesThis study aimed to determine whether altitude, population density, and percentage of population in total poverty are associated with COVID-19 incidence per 1000 inhabitants and COVID-19 case-fatality rate in Peru, from 2020 to 2022. MethodsThis study utilized a multiple group ecological design and relied on secondary databases containing daily records of COVID-19 positive cases and deaths due to COVID-19. An epidemiological analysis was performed, subsequently processed using a random effects model. ResultsAs of August 2022, Peru had recorded a total of 3,838,028 COVID-19 positive cases and 215,023 deaths due to COVID-19. Our analysis revealed a statistically significant negative association between altitude and COVID-19 incidence (aBETA: −0.004; Standard Error: 0.001; p < 0.05). Moreover, we observed a positive association between population density and incidence (aBETA: 0.006; Standard Error: 0.001; p < 0.05). However, we found no significant association between the percentage of population in total poverty and COVID-19 incidence. ConclusionOur study found that an increase in altitude was associated with a decrease in COVID-19 incidence, while an increase in population density was associated with an increase in COVID-19 incidence. High altitude, population density and percentage of population in total poverty does not change case-fatality rate due to COVID-19.

Variations in complete blood counts and cellular morphological alterations in blood smear of COVID-19 patients from a tertiary care center

Corona viruses are a large family of viruses of which Corona virus disease 19 (COVID 19) infection evokes a hyperinflammatory response and severe acute respiratory distress syndrome in human beings. Complete blood count evaluation and peripheral smear examination is an essential tool for early diagnosis and management of COVID-19 positive patients. This study was aimed to evaluate the variations in complete blood counts (CBC) and changes in the cellular morphology in the blood smear in patients affected with COVID-19. This was an observational study, undertaken in a tertiary care center. The study included baseline blood samples from 96 proven COVID-19 positive cases. The samples were subjected for automated complete blood count analysis and peripheral smear examination.Thequantitative data were expressed by mean and Standard deviation. Qualitative categorical variables were expressed as numbers and percentages. Out of the total 96 cases, the most common quantitative finding was neutrophilic leukocytosis with relative lymphopenia. Five cases of absolute lymphopenia was observed. Majority of the cases had normal hemoglobin values and normal platelet counts. The characteristic morphological changes were seen in neutrophils and lymphocytes. The neutrophils showed presence of toxic granules, cytoplasmic vacuolations and hypolobation.The morphological alteration in lymphocytes included reactive lymphocytes with plasmacytoid and monocytoid morphology, large granular lymphocytes and Turk cells. Infrequent finding of leukoerythroblastic reaction was also observed in our study.To summarize, hematological investigation of COVID 19 cases may show several abnormal findings. Quantitative findings like Neutrophilia and lymphopenia were characteristic, whereas, the most common morphological alteration observed was atypical lymphocytes. Quantitative and qualitative alterations of hematological parameters are one of the investigations which would aid timely diagnosis of the disease. Toxic granules and cytoplasmic vacuolations in neutrophils may indicate ongoing sepsis in COVID 19 patients which in turn aids in efficient management of the patients to reduce the mortality rate.

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.