Analysis Of COVID-19 Cases Research Articles

Impact of COVID-19 pandemic on the incidence and severity of myasthenia gravis in Korea: using National Health Insurance Service database.

This study investigated the impact of COVID-19 pandemic on the incidence and severity of myasthenia gravis (MG) using the National Health Insurance Service (NHIS) database in Korea. We analyzed data from patients with MG in the NHIS registry from 2015 to 2021. MG was defined as (1) patients aged ≥18 years with the G70.0 code, and (2) patients who visited tertiary hospitals regarldless of department in Korea (outpatient clinics at least twice or hospitalization at least once), and (3) patients who were prescribed pyridostigmine as MG medications at least once. We designated pre-COVID-19 as 2019 and post-COVID-19 as 2021 and analyzed the MG incidence and prevalence in 2019 and 2021. We compared the clinical data of patients with MG between the two years. MG exacerbation was defined as the administration of intravenous immunoglobulin or plasma exchange. Analysis of COVID-19 cases was conducted using an integrated database from the Korea Disease Control and Prevention Agency and NHIS. Patients with MG were divided into two groups according to COVID-19 status to compare their clinical characteristics. A total of 6,888 and 7,439 MG cases were identified in 2019 and 2021, respectively. The standardized incidence was 1.56/100,000 in 2019, decreasing to 1.21/100,000 in 2021. Although the frequency of MG exacerbations was higher in 2019, there were no differences in the number and duration of hospitalizations, duration of ICU stays, hostalization expense, and mortality between 2019 and 2021. Patients with MG and COVID-19 had a higher frequency of MG exacerbations than patients without COVID-19, but there were no differences in the number and duration of hospitalizations, hospitalization expense, and mortality. This study was the first nationwide population-based epidemiological study of MG during COVID-19 pandemic in Korea. The incidence of MG decreased during COVID-19 pandemic, and the severity of MG was not affected by COVID-19.

Implementation of a quantum machine learning model for the categorization and analysis of COVID-19 cases

Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate.

Analysis of COVID-19 cases and comorbidities using machine learning algorithms: A case study of the Limpopo Province, South Africa

This study examined the biological, social, and clinical risk factors for mortality in coronavirus of the year 2019 (COVID-19) hospitalised patients. The population of the study is prone to COVID-19, thus understanding the most common traits and comorbidities of people who were affected is crucial in reducing its consequences. In this study, four supervised machine learning algorithms were implemented and compared to predict the mortality rate based on the explanatory variables across the five districts of Limpopo Province in South Africa. The data was obtained from Limpopo Department of Health. Prediction about the chances of dying from COVID-19 disease was made using logistic regression, random forest, support vector machine, and decision tree algorithms on the dataset of 20,592 records with twenty-one attributes. Due to the imbalanced nature of the data, Random Over-Sampling Examples (ROSE) were employed to balance our data for more accurate classification effectively. The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. We used 70% of the data for training, while 30% was selected for testing the predictive algorithms. A technique called Step Akaike's Information Criterion (StepAIC) was deployed to reduce the insignificant variables from the full model of the logistic regression. According to the findings of the study, among the four algorithms tested, random forest had the highest recall rate for predicting mortality at roughly 79 percent compared to the other three algorithms. Accordingly, we conclude that random forest algorithm is appropriate for predicting the chances of patients dying from COVID-19 based on the attributes of the five districts of Limpopo Province. In terms of the features and their importance, a function called Variable Importance (VarImp) was used to check which of the attributes have predictive power on the outcome variable (discharged status). The findings revealed that age, ever ventilated, ever oxygenated, intensive ward upon admission, Waterberg district, and private facility type were among the risk factors that could be selected for the logistic regression model to predict mortality in hospitalised patients. This implies that special attention should be given to these identified variables. The random forest model is adequate to establish these factors since the findings reveal that a fairly considerable percentage of explained variation would correctly classify 79% of the cases. The novelty of conducting research on the analysis of COVID-19 cases and comorbidities using machine learning algorithms lies in the potential to uncover new insights and patterns that might not be immediately apparent through traditional statistical methods. Machine learning algorithms can quickly and accurately analyze large datasets and identify complex relationships between variables, which could provide valuable information for public health officials and policymakers.

Clustering and Forecasting of Covid-19 Data in Indonesia

Indonesia reported its first case of Covid-19 in March 2020, which was suspected to have been infected by a foreigner who visited Indonesia. The distribution of cases that occurred in Indonesia has an uneven frequency considering that Indonesia is an archipelagic country, in the analysis of Covid-19 cases in Indonesia, there are many provinces and some have the same pattern of case characteristics. time series so that forecasting analysis can be used. So that clustering analysis and forecasting of Covid-19 data can be used in Indonesia. The analysis was carried out with 2 stages of analysis, namely clusters using the clustering hierarchy method and forecasting using the ARIMA method. By using 288 data from January 1, 2021 – October 15, 2021, the results show that the daily Covid-19 cases by province in Indonesia can be grouped into 2 clusters, in the forecasting analysis only one province is taken from each cluster used in determining the model, cluster 1 used data from the province of Banten and cluster 2 used data from the province of West Java. By using R software, a model for each cluster is obtained, namely ARIMA(0,1,1) for cluster 1 and ARIMA(2,1,2) for cluster 2. From the forecasting results obtained data until October 30, 2021 shows the number of cases tends to be constant.

Root cause analysis of COVID-19 cases by enhanced text mining process

<p>The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.</p>

COVID-19 in Saudi Arabia: An Overview.

BackgroundSaudi Arabia, a prominent Arabian country, has 35. 3 million persons living in 2.2 million square kilometers, undergone serious threats recently due to the COVID-19 pandemic. With the built-in infrastructure and disciplined lifestyle, the country could address this pandemic.AimsThis analysis of COVID-19 cases in Saudi Arabia attempts to assess the situation, explore its global percentage share, percentage of population affected, and local distribution from the beginning of infection until recently, tracing historical developments and changes.Data and MethodsThis analysis made use of data released by the Ministry of Health on a daily basis for a number of parameters. They are compiled on an excel sheet on a daily basis: the dataset has undergone rigorous analysis along with the trends and patterns; proportion to the world statistics and geographic distribution.ResultsCOVID-19 spread rapidly in the country with periodic variations, during June-August, 2020. But, recoveries accelerated in the period, thus bridging the gap of increasing infections. In comparison with the world statistics, the country proportions are lower, while the percentage of population affected is similar. It appears that the intensity varied across all 13 administrative areas.ConclusionCOVID-19 transmission since March 2020 is considered to be widespread, creating excess burden on the public health system, delineated into stages (early infection, rapid spread, declining, stabilizing, and second wave). Control measures are set, stage-wise, without impinging upon normal life but to ensure that the proportion of globally affected persons is lesser than the population share: credit goes to the Ministry of Health. Area-wise spread depends largely on population density and development infrastructure dimensions. Ultimately, the disciplined life in compliance with law and order paved the way for effective program implementation and epidemic control.

The Role of Staff in Transmission of SARS-CoV-2 in Long-Term Care Facilities: Analysis of COVID-19 Cases in Fulton County, Georgia, March 2020 to September 2021

The Role of Staff in Transmission of SARS-CoV-2 in Long-Term Care Facilities: Analysis of COVID-19 Cases in Fulton County, Georgia, March 2020 to September 2021

An Analysis of COVID-19 Cases in G20 Countries and History of Previous Pandemics

This research paper focusses on the recent impact of Covid-19 on the G20 countries on basis of the number of cases and the number of deaths in several aspects. This paper also provides a brief history of the previously occurred pandemics and epidemics. A statistical analysis was conducted for 19 countries of the G20 assembly and presented in the paper. This topic was chosen in interest of recent events of the Covid-19 pandemic and its extensive effect on the world at large. The dataset consisting of records from nineteen countries was chosen as a part of the analysis. Apart from being involved in the G20 summit, these countries are looked upon by other countries of the world due to their economic and overall development. Further, past history of some pandemics and epidemics were taken into study

An Analysis of COVID-19 Cases in G20 Countries and History of Previous Pandemics

This research paper focusses on the recent impact of Covid-19 on the G20 countries on basis of the number of cases and the number of deaths in several aspects. This paper also provides a brief history of the previously occurred pandemics and epidemics. A statistical analysis was conducted for 19 countries of the G20 assembly and presented in the paper. This topic was chosen in interest of recent events of the Covid-19 pandemic and its extensive effect on the world at large. The dataset consisting of records from nineteen countries was chosen as a part of the analysis. Apart from being involved in the G20 summit, these countries are looked upon by other countries of the world due to their economic and overall development. Further, past history of some pandemics and epidemics were taken into study.

Features of the Course of COVID-19 in Health Care Workers of a Non-infectious Hospital

Background: Health Care Workers (HCWs) are at high risk of SARS-COV-2 infection and COVID-19 disease, not only in the chain of care for COVID-19 patients but also in non-infectious health care organizations that provide routine medical care to the population. Objective: To analyze features of the course of COVID-19 in HCWs of a non-infectious hospital (non-IH). Methods and Materials: A retrospective analysis of COVID-19 cases of HCWs of a non-infectious hospital (non-IH) due to a local outbreak during the “second wave” of the pandemic, despite the use of PPE. Results: 7 HCWs out of 23 employees were infected and had a confirmed COVID-19 infection. All pointed to the presence of contact at work (patients, colleagues) as the main possible contamination factor. The development of COVID-pneumonia was observed in 4 persons; 2 of them were hospitalized. The most common symptoms were fever with a maximum temperature rise of 37.8-39 °C, weakness, chest pain, cough, and loss of smell. The duration of the persistence of symptoms of the disease was up to 16 days. The elimination of coronavirus in most patients was noted after the 10th day. Conclusion: HCWs in non-IH providing routine medical care are at increased risk of contracting COVID-19 due to contact with infected patients or colleagues. The development of COVID-19 disease in HCWs leads to long-term disability, which creates tension in the work of non-IH. To reduce the risk of infection for non-IH HCWs, stated precautions are necessary: strict adherence to PPE use, mandatory testing of SARS-COV-2, and planning observational wards for unexamined patients hospitalized for medical care for other indications.

AB0670 THE COURSE AND OUTCOMES OF COVID-19 IN PATIENTS WITH ANCA-ASSOCIATED SYSTEMIC VASCULITIS, RECEIVING ANTI-B CELL THERAPY WITH RITUXIMAB

Background:Currently, the issues of the effect of anti-B cell therapy on the risk of severe course of COVID-19 in patients (pts) with ANCA-associated vasculitis (AAV) has not been completely studied.Objectives:We present an analysis of the COVID-19 incidence and outcomes in 126 AAV pts treated with rituximab (RTX) from one rheumatology center registry.Methods:Data were collected with telephone/online surveying between 11-15th November 2020 from all 126 AAV pts (58- granulomatosis with polyangiitis, GPA; 38- microscopic polyangiitis, MPA; 22- eosinophilic granulomatosis with polyangiitis, EGPA (54% ANCA-negative); and 8- uncertain AAV). Pts age was 51 (20-81) yr, 62% women. Due to AAV activity or risk of relapse, during the pandemic RTX was prescribed in 48% pts.Results:The incidence of COVID-19 in AAV pts was 3.5% during the first 3 months of the pandemic, the course was uneventful, and all pts recovered. Since September 2020 a 4-fold increase in the incidence alongside with more severe course of COVID-19 were reported. After 8 months of the pandemic, COVID-19 was diagnosed in 21 (16.6%) pts with median age 55 yr, 57% women. COVID-19 incidence was lower in GPA (15.5%) vs MPA (21.1%) or EGPA (27.7%). The mortality rate was 14.3% (2- MPA, 1- GPA). When analyzing the 5-year survival rate of AAV pts treated with RTX, prognosis worsening was noted; in 2020 there were 3 deaths due to COVID-19, in the previous 5 years- only 2 deaths.Conclusion:Taking into account the fact the mechanisms of AAV and severe COVID-19 are largely synergistic (Table 1), primarily in the context of obstructive thromboinflammation as manifestation of acute inflammatory syndrome in COVID-19 [1], AAV can potentially contribute to the severe course of COVID-19. Anti-CD20 therapy seems to have a protective effect against severe COVID-19 [2], which, however, can be insufficient in AAV pts with active disease and multiple organ damage. Further analysis of COVID-19 cases in pts with AAV and other rheumatic diseases is exceptionally important.Table 1.Comparison of AAV and severe COVID-19ParameterAAVSevere COVID-19Lung damageHigh frequency of lung damagePossibly destructive lung damageDamage to other organsTypically affects the upper and lower airways and kidneys; Other organs can be involvedPossible extrapulmonary localizationVascular involvementSystemic necrotizing vasculitis of small vessels; In the active phase, hypercoagulationObstructive thromboinflammation of the microcirculation of lungs and extrapulmonary vessels; Increased incidence of Kawasaki syndrome during the pandemicInflammationHigh or very highVery highAutoantibodiesANCA pathogenetic roleDominant extrafollicular B cell responses; Сorrelation between SARS-CoV-2 antibody titer and severity of COVID-19; High titers of IFN-α2/ω autoantibodiesNeutrophilNeutrophils are the most important effector cells in the pathogenesisChanges in the morphology of neutrophils were noted; Netosis, the release of proteases from neutrophils promote the complement system activation, hypercoagulation, endotheliitis, production of pro-inflammatory cytokines; An increase of neutrophils in the blood and bronchoalveolar lavage fluid were notedComplementComplement is crucial in the pathogenesis; C5a receptor antagonist Avacopan efficacyActivation of alternative/lectin complement pathways; C5 antagonist Eculizumab has been reported to be effectiveTreatmentImmunosuppressants; In the active phase, anticoagulantsAnticoagulants; Glucocorticoids, pro-inflammatory cytokine antagonists, JAK inhibitors, cyclosporinePrognosisSeriousHigh mortality rate

Analysis of COVID-19 cases in adult cystic fibrosis patients in Saint-Petersburg and the Leningrad region

March 11, 2020 WHO has declared the COVID-19 coronavirus pandemic. The European Society for Cystic Fibrosis (ECFS) has developed the ECFSCOVID- 19 surveillance program to collect information on the characteristics of COVID-19 in patients with cystic fibrosis (CF). CF centers in the Russian Federation also joined the ECFS study. The ECFS collected information only through national Registers. In our country, the national register of CF patients has existed since 2011, its data is included in the European Register. Тo analyze the prevalence and course of COVID-19 in adult CF patients in Saint-Petersburg and the Leningrad region.Methods. From 11.03.20 to 24.12.20, 10 cases of adult CF patients (4 women, 6 men) with COVID-19 were analyzed in Saint-Petersburg and the Leningrad region. At the beginning of the study, 56 adult CF patients lived in Saint-Petersburg and Leningrad Region. All patients were monitored remotely by telephone, in case of ARVI symptoms, disease exacerbation, a PCR smear was performed on SARS-CoV-2, therapy was prescribed remotely, and inpatient treatment was prescribed if indicated.Results. COVID-19 was diagnosed in 10 adult CF patients aged 21 to 43 years. Two patients (32-year-old male and 31-year-old female with severe mixed cystic fibrosis form) were hospitalized with polysegmental pneumonia. 8 people were treated on an outpatient basis. Only 1 outpatient patient aged 43 years had pronounced clinical manifestations of COVID-19, the condition was regarded as moderate, the remaining outpatient patients suffered from a mild ARI disease. All patients were treated with positive dynamics. During the period of this follow – up, 3 CF patients (2 men, 1 woman) died, the death cause was the respiratory failure progression. Clinical and radiological, repeated laboratory data for COVID-19 were not obtained. At the point of December 24, 2020 53 adult CF patients are alive.Conclusion. According to our data, the incidence of COVID-19 among adult CF patients in Saint-Petersburg and the Leningrad Region was 17.85%, which is a lower indicator than in the general population. The disease was rarely severe, and effective treatment was noted in outpatient settings. There were no deaths from COVID-19.

The course and outcomes of COVID-19 in patients with ANCA-associated systemic vasculitis, receiving biological therapy (Rituximab, Mepolizumab): The results of the first 8 months of the pandemic

Objective. Currently, the issues of the effect of anti-B cell therapy or inhibitor of interleukin 5 on the risk of COVID-19 infecting and outcomes in patients with ANCA-associated vasculitis (AAV) has not been completely studied. We present an analysis of the COVID-19 course and outcomes in AAV patients treated with rituximab or mepolizumab from one rheumatology center registry. Methods. From November 11 to November 15, 2020, a cross-sectional study was conducted using telephone and online surveys, and information was collected from all 128 AAV patients treated with rituximab in V.A. Nasonova Research Institute of Rheumatology. Patients mean age was 51 (20–81) years, 61.7% were women. Granulomatosis with polyangiitis (GPA) was diagnosed in 58 patients, microscopic polyangiitis (MPA) – in 38, eosinophilic granulomatosis with polyangiitis (EGPA) – in 24 (including 54.2% of ANCA-negative cases), and AAV with uncertain nosological affiliation – in 8 patients. Due to the disease activity or a high risk of AAV recurrence during the pandemic rituximab was prescribed in 60/126 (47.6%) patients, and mepolizumab – in 6 cases. Results. In the spring of the pandemic (until May 2020), the incidence of COVID-19 in AAV patients treated with rituximab was 4.3%, the disease course was relatively favorable. All patients recovered. At month 3–6, antibodies to SARS-CoV-2 IgG persisted in only 1 out of 4 patients. Since September 2020, the incidence has increased 3-fold, with a more severe course of COVID-19. In total, in the period until November 11, 2020, COVID-19 was diagnosed in 17.2% (22/128); the mean age of patients was 55 (25–81) years; 54.5% were women. 21/22 patients were on rituximab therapy, 2 patients had mepolizumab therapy (including 1 case after previous rituximab therapy). COVID-19 incidence was lower in patients with GPA (15.5%) vs MPA and EGPA (21.1% and 20.8% respectively). The mortality rate was 13.6%, including 2 patients with MPA and 1 patient with GPA. When analyzing the 5-year survival rate according to the registry of AAV patients treated with rituximab, prognosis worsening was noted; in 2020 there were 3 deaths due to COVID-19, in the previous 5 years – only 2 deaths. Discussion. Taking into account the fact the mechanisms of AAV and severe COVID-19 are largely synergistic (primarily in the context of microvascular COVID-19 lung vessels obstructive thromboinflammatory syndrome as manifestation of the acute inflammatory syndrome), the activity of AAV can potentially contribute to the disease onset and a severe course of COVID-19. Given the previously published information on the use of rituximab during the COVID-19 pandemic for various diseases, it seems that B cell depletion, without reducing the risk of infection, may have a protective effect with regard to the risk of severe/catastrophic COVID-19, which, however, can be insufficient in AAV patients. Further analysis of COVID-19 cases in patients with AAV and other immuno-inflammatory rheumatic diseases is exceptionally important.

An Analysis of COVID-19 Cases in Nepal: A Modeling Approach

Unlike previous coronaviruses infections, COVID-19 has badly affected not only the health of people but also the socioeconomic activities of Nepal. It would help the government of Nepal to manage this crisis if a proper mechanism to predict COVID cases has been developed. This study aims to look for patterns of confirmed, recovery and death cases. Moreover, it tries to check whether Gompertz and Logistic model would be able to read the patterns of total confirmed and death cases. It also forecasts the total number of confirmed as well as death cases. Data from January 23, 2020 to October 30, 2020 obtained from the website of Wikipedia are used for analysis. Gompertz and Logistic models were fitted to the total number of confirmed and death cases and models are compared based on various criteria. Besides, an automatic ARIMA model was used to predict cumulative confirmed and death cases and the accuracy of the model was also checked. ARIMA model forecasted 347,812 confirmed cases and 1,754 death cases till December 31, 2020. At 95 % confidence interval, the confirmed cases were expected between 273,889 and 421,734 whereas death cases were estimated from 1,387 to 2,119. Both models were fitted well in both total confirmed cases and total death cases. It was found that the Logistic model fits better in total confirmed cases whereas in total death cases, the Gompertz model was better. ARIMA model precisely forecasted the number of confirmed and death cases.

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities.

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = –0.203, p < 0.0001) and deaths (Moran I = –0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

Sex differences in COVID-19 case fatality: do we know enough?

Sex differences in COVID-19 case fatality: do we know enough?

Analysis of COVID-19 Cases and Public Measures in China.

This paper briefly analyzes COVID-19 cases during Wuhan lockdown and travel restrictions on 23 January 2020 to 23 June 2020, which included total confirmed, in critical condition, deaths, recovered, and suspected cases in China. Results showed that there were 28,942 suspected cases on February 8, 2020, at the peak; then, it almost declined continually to only several cases. Total confirmed cases were more than 80,000 on March 1, 2020, but less than 84,000, and deaths were more than 3000 on March 4, 2020, but less than 4640, totally, thanks for the right public measures for COVID-19 in China, such as the Wuhan City lockdown and travel restrictions for isolation; positive screening and testing; and establishing a Huoshenshan hospital, a Leishenshan hospital, and a number of Fangcang shelter hospitals, traditional Chinese medicine and a combination of Chinese and western medicine, and the launch of the clinical trials of antiviral drugs (Lianhua Qingwen, remdesivir, and chloroquine). In addition, the iRT-ABCDEF program is very useful to control domestic, imported, and asymptomatic cases. Cases in critical condition decrease continually after the peak of 11,977 cases on February 18, 2020, and recovered cases increase continually to over 78,400 cases due to these right public measures and effective treatments. In recent months, there are only 2 deaths and only about ten cases in critical condition. All in all, these public measures in China are confirmed to be very effective and are worth conducting in countries worldwide.

Analysis of Covid-19 Cases in the Philippines Using Sigmoidal-type Models

In this paper, a new method of curve-fitting analysis is introduced, and applied to several sigmoidal-type models in order to come up with an estimate of the accumulated number of COVID-19 positive cases in the Philippines The best fit model is obtained using a new criterion and will then be utilized for forward predictions of COVID-19 cases in the Philippines © 2020, Institute of Advanced Scientific Research, Inc All rights reserved

Evolução epidemiológica da COVID-19 no Estado do Piauí em 2 meses

A COVID-19 é uma doença causada pela nova cepa viral da Família Coronaviridae (Sars-Cov-2), que surgiu na província de Hubei, na China, em Dezembro de 2019, e vem se disseminando rapidamente pelo mundo desde então. Assim, frente à importância da infecção pelo Sars-Cov-2 para o atual cenário mundial, que também se reflete no Piauí, e devido à relevância de dados epidemiológicos para o estabelecimento de políticas de Saúde Pública mais efetivas, este estudo objetivou analisar a evolução epidemiológica dos casos de COVID-19 no estado do Piauí em 2 meses. Trata-se de um estudo ecológico, retrospectivo, realizado a partir de dados secundários. Calculou-se a taxa de mortalidade, a ocupação de leitos e o isolamento social médios, com realização de correlações entre estes por meio do coeficiente de Pearson e avaliação da significância estatística. No período analisado (2020), notificou-se: 5.101 casos e 164 óbitos, com uma taxa de mortalidade de 34,4/1000 em Abril e de 31,9/1000 em Maio. Observou-se relações inversas entre o número de casos e a taxa de mortalidade (r = -0,491, p = 0,049), entre a ocupação de leitos e o isolamento social médios (r = -0,415, p = 0,001) e entre o isolamento social médio e o número de casos notificados (r= -0,329, p = 0,048). Os resultados reforçam a importância do isolamento social como medida de enfrentamento da COVID-19 no Piauí.

Trend analysis of Covid-19 cases in Indian subcontinent: Prediction comparison and psycho-social interventions

The outbreak of the novel coronavirus (COVID-19) which started in December, last year has claimed thousands of life around the world. Utilizing the existing COVID-19 database, this article attempted to compare the status of infection cases and death of the four significantly affected nations of Indian subcontinent (ISC): Bangladesh, India, Maldives and Pakistan with the top 4 affected nations of the world, today. This article is also an attempt to conduct a trend analysis to predict the upcoming cases in exclusively the ISC countries. The results suggest that Pakistan and India should be well prepared in the coming one month. Finally, the needs of psychosocial support and the psychotherapeutic interventions were elucidated to help preserve the mental health of the people in this hour of crisis.