Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh.

Background: Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection. Methodology: In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models. Result: The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection. Conclusion: Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Oral manifestations in COVID-19 patients among the Bangladeshi population: a cross-sectional study

Background: COVID-19 is a multi-system all-pervasive disease with protean manifestations, and its major signs and symptoms, such as incessant dry cough, fever, and pneumonia, are well known. Yet, its mucocutaneous manifestations, particularly those of the oral cavity, appear to be little recognized. This may be due either to the rarity of oral manifestations of COVID-19, or poor detection of such symptoms by attending physicians who may do only a cursory examination of the oral mucosa because of the overwhelming gravity of the other major systemic presentations. Nevertheless, there are now a considerable number of reports, including systematic reviews, on oral manifestations of COVID-19 in the literature. This observational study was performed to determine the oral manifestations among COVID-19 patients. Material & Methods: A cross-sectional study was carried out among COVID-19 recovered patients. 120 Covid 19 recovered patients were purposively selected as study samples. All the samples diagnosed as mild and moderate cases of COVID-19 disease were selected based on inclusion and exclusion criteria. Results: The study comprised the majority of males (68%) where females represent (32%) of the study population and the mean age was 39.3+or-12.4. Oral manifestations among study subjects during and after the disease illness including loss of taste being the commonest symptom (40%), followed by erythema and coated tongue (7.5%), mouth ulcerations (6.7%) and dry mouth (1.7%). The study revealed that the 41-60 age group subjects represented the highest (43%) oral manifestations. Conclusions: Early identification of oral symptoms in COVID-19 recovered or suspected cases can help a dentist or a general physician to diagnose high-risk groups, mitigate transmission, and promote overall health.

Post-Pandemic Sentiment Analysis Based on Twitter Data Using Deep Learning

Since January 2020, COVID-19 has been spreading over the world and has been declared a pandemic. Nation and society are growing scared of it as fresh instances and mass deaths increase daily. India was one of the major countries to suffer the consequences of COVID-19 during that phase as multiple waves hit India. Many social media channels were being used by people from all over the country to discuss this pandemic and its aftereffects. One of the most popular ways to share opinions or judgments today is through social media. Therefore, machines are continuously being developed to analyze what people post on social networking sites like Twitter, Facebook, Instagram, and other platforms thanks to advancements in current computing technology. Based on their mood, these ideas or points of view can be grouped and examined. In this paper, we used tweets collected from Twitter to analyze the sentiment that people conveyed on social media after the second wave of Corona Virus. The sentiment of the tweets has been divided into five categories: "Strongly Negative", "Negative", "Neutral", "Positive"and "Strongly Positive". First, we classify data using Python's Vader. We have trained a model using our own labeled dataset and evaluated its performance using Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). © 2022 IEEE.

The Effects of SARS-CoV-2 Infection on Renal Function in Patients with Hemodialysis

Objective: This study aims to demonstrate the impact of "SARS-CoV-2" infection on renal function in patients who have undergone hemodialysis in the past. Methodology: Telomerase Reverse Polymerase Chain Reaction in Real Time (RT-Real time PCR) To verify "SARS CoV-2" infection, RT-PCR was used, moreover pre and post urea and creatinine tests were confirmed by COBAS INTEGRA 400 plus analyzer was automated qualitative assays rapidly detected Creatinine, urea, and diabetes Mellitus levels. Result(s): The mean of pre-creatinine levels was 7.3336. The post-creatinine levels (11.8276) significantly increased after "SARS-CoV-2" infection with a P-value of 0.001. The mean of pre-urea levels was 163.6724. The post-urea levels (213.706897) significantly increased after "SARS-CoV-2" infection with a P-value of 0.001. Conclusion(s): SARS-CoV-2 infection in patients with pre-existing hemodialysis leads to increasing kidney dysfunction with or without comorbidities (diabetes mellitus and hypertension). Moreover, the old patients with pre-existing hemodialysis are found to be at higher risk of renal dysfunction during "SARS-CoV-2" infection than the younger groups. Copyright © 2022, Bahrain Medical Bulletin. All rights reserved.

Evaluating knowledge, awareness and associated water usage towards hand hygiene practices influenced by the current COVID-19 pandemic in Bangladesh.

Hand hygiene is considered as one of the most effective ways for preventing transmissible diseases, especially for preventing virus-borne diseases. The study has been conducted to evaluate changes in knowledge, awareness and practices of hand hygiene due to the outbreak of the coronavirus disease (COVID-19) in Bangladesh. The potential factors influencing human behaviours for maintaining hand hygiene have also been explored. Moreover, a probable increase in daily water demand associated with the changed situation has been assessed. An online survey was performed among a total of 367 Bangladeshi residents about their practices of hand hygiene during pre-corona, corona, and of their perceived future practices at post-corona period. It has been observed that a significant percentage (62.1%) of the respondents have received basic hygiene education at any level of their academic education. Nevertheless, their hygiene practices were very poor before the COVID-19 pandemic. The outbreak of the COVID-19 has reinforced their previous knowledge and greatly influenced their behavioural changes towards practicing hand hygiene as per World Health Organization guidelines for preventing the virus outbreak. The changes, however, have created increased water demand. The estimated water usage is found to be 2.68 times (9.15 L/c/d) and 2.52 times (8.59 L/c/d) higher in the corona and post-corona period respectively than that of the pre-corona situation (3.41 L/c/d). The principal component analysis (PCA) elucidated that an individual's practice of hand hygiene was associated with income, level of academic and hygiene education, and the COVID-19 outbreak itself. Moreover, the influence of hygiene education and COVID-19 outbreak affecting the duration of handwashing are found highly significant (p-value < 0.001) from the regression analysis. Raising awareness towards behavioural change of an individual about water usage, improvement of academic curriculum regarding hand hygiene and provision of water pricing are recommended to attain sustainable development goals of the country.

Revealing Influences of Socioeconomic Factors over Disease Outbreaks

The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction. © 2022 ACM.

Investigation of Some Biochemistry Markers in COVID-19 Vaccinated Iraqi Individuals

Background: Serious intense respiratory disorder Covid-19 is a causing respiratory infection known as Corona virus. This novel corona virus transmits from one human to another and has caused significant mortality overall prompting the continuous pandemic. Also, illness seriousness varies extensively from one person to another. Subjects: fifty individuals (n = 100) with age ranged (20-65 years) were enrolled in this study. Laboratory parameters were performed of baseline and 14 days after first dose and 14 days after second dose from covid-19 vaccination. Results: All people in this study received the Pfizer-BioNTech vaccine and none systematic side effects were observed in vaccinated subjects during the study period. Conclusions: From the results of present study, conclusions could be that all parameters are non-significant and without the appearance of abnormal biochemical signs or increase in coagulation or changes in basic body functions following the received the Pfizer vaccine.

Inventory decisions on the transportation system and carbon emissions under COVID-19 effects: A sensitivity analysis.

The COVID-19 pandemic has created multiple problems in the existing transportation system. The contribution of this study is to guide logistics managers as they make ordering decisions within a disrupted transportation system. In the overall supply chain system, inventory decisions have been either compromised or challenged. Traditional inventory decisions that consider preplanned transportation facilities (and speeds) are currently becoming obsolete, predominantly in post-COVID times due to delays in the delivery of products and higher delivery costs. Therefore, businesses such as retailers must align ordering and pricing decisions to maintain a sustainable profit. To address this issue, this study investigates optimum inventory decisions under the pandemic's effects while considering the transportation cost as proportional to COVID-19 intensity. This study also considers product deterioration, time-dependent holding costs, price-dependent demands, and carbon emissions from vehicle operation and intends to establish a harmonious relationship among these attributes. The optimization of green technology investment is studied to reduce emissions due to transportation. Some theoretical derivations and numerical examples are given, and they are followed by a sensitivity analysis to extract important managerial insights into the effect of COVID-19. The manager can set an optimal selling price and the cycle length by carefully planning the number of trips in considering the rate of the outbreak and its effect on the increasing transportation cost.

COVID-19 among staff and their family members of a healthcare research institution in Bangladesh between March 2020 and April 2021: a test-negative case-control study.

OBJECTIVE: To identify factors associated with COVID-19 positivity among staff and their family members of icddr,b, a health research institute located in Bangladesh. SETTING: Dhaka, Bangladesh. PARTICIPANTS: A total of 4295 symptomatic people were tested for SARS-CoV-2 by reverse-transcription PCR between 19 March 2020 and 15 April 2021. Multivariable logistic regression was done to identify the factors associated with COVID-19 positivity by contrasting test positives with test negatives. RESULT: Forty-three per cent of the participants were tested positive for SARS-CoV-2. The median age was high in positive cases (37 years vs 34 years). Among the positive cases, 97% were recovered, 2.1% had reinfections, 24 died and 41 were active cases as of 15 April 2021. Multivariable regression analysis showed that age more than 60 years (adjusted OR (aOR)=2.1, 95% CI 1.3 to 3.3; p<0.05), blood group AB (aOR=1.5, 95% CI 1.1 to 2; p<0.05), fever (aOR=3.1, 95% CI 2.6 to 3.7; p<0.05), cough (aOR=1.3, 95% CI 1.1 to 1.6; p<0.05) and anosmia (aOR=2.7, 95% CI 1.3 to 5.7; p<0.05) were significantly associated with higher odds of being COVID-19 positive when compared with participants who were tested negative. CONCLUSIONS: The study findings suggest that older age, fever, cough and anosmia were associated with COVID-19 among the study participants.

A Convolutional Neural Network Model for Screening COVID-19 Patients Based on CT Scan Images

The novel coronavirus (COVID-19) spread all over the world within a few months and turned into a pandemic. Early diagnosis is the only way to combat this pandemic by isolating the affected cases from healthy ones for refraining it from further spreading. At present, RT-PCR is extensively used for screening coronavirus cases, however, WHO stated that it suffers from low sensitivity and low specificity in the early-stage patients. Recent studies advise that the CT scan image embraces key features for detecting this disease. The application of deep learning approaches combined with CT images could be useful for the precise diagnosis of positive coronavirus patients. In this research, we have employed the Convolutional Neural Network (CNN) architecture of deep learning on publicly accessible CT images dataset to build a prediction model for classifying positive COVID-19 from other pulmonary diseases and healthy patients. Moreover, this prediction model has also been utilized for identifying COVID-19 cases from other pulmonary diseases, which is a binary classification. In ternary classification, our proposed CNN model has obtained an accuracy of 98.79%, a precision of 94.98%, a sensitivity of 98.85%. In contrast, for binary classification, it has acquired an accuracy of 98.79%, a precision of 94.98%, a sensitivity of 98.85%. Therefore, this proposed model can be a faster and alternative tool or even a supportive tool along with RT-PCR in rural areas of many countries where there is a scarcity of testing kits and expert physicians. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 transmission flow through the stigmatization process in Bangladesh: A qualitative study

Introduction Coronavirus disease (COVID-19) patients and survivors face stigma, discrimination, and negligence. The motives for and the different types and consequences of COVID-19-related stigmatization remain underexplored in Bangladesh. Therefore, this study examined how the COVID-19 stigmatization process is interlinked with transmission flow. Methods Using a qualitative research design, we conducted 20 in-depth interviews with infected and suspected caregivers and five key informant interviews with physicians, local media representatives, leaders, law enforcement officials, and local administrative officials in three divisional cities of Bangladesh. We performed thematic analysis to analyze the data. Results Participants expressed their experiences with multiple subthemes within three themes (stigma related to symptoms, stigma associated with isolation and quarantine, and stigma associated with health services). Participants reportedly faced stigma, for example, exclusion, hesitation to interact, avoidance, bullying, threat, and negligence caused by misinformation, rumors, and fear. Stigmatized individuals reportedly hid their symptoms and refrained from seeking healthcare services, contributing to COVID-19 transmission flow. Conclusion Revealed insights may contribute to effective prevention, control, and management of such an emerging pandemic. Further in-depth exploration of such stigmatization process will enrich unexpected outbreaks management effectively.

COVID-19 vaccine acceptance in South Asia: a multi-country study.

OBJECTIVES: With COVID-19 vaccination underway, this study aimed to understand belief, attitude and intention of the people in the South Asia region towards the vaccine. METHODS: We conducted a cross-sectional study using semi-structured questionnaires among 18201 individuals in four South Asian countries; Bangladesh, India, Pakistan, and Nepal between January 17 and February 2, 2021. We used the Health Belief Model (HBM) to identify the predictors related to vaccine acceptance. STATA (v16.1) was used for all analyses. RESULTS: The percentage of respondents willing to be vaccinated against COVID-19 was 65%, 66%, 72% and 74% for Bangladesh, India, Pakistan and Nepal, respectively. Perceived destructive impact of COVID-19, positive perception of vaccines and concern about possible side effects were significant in modifying respondents' intentions.. In multivariable logistic regression, age, sex, marital status, education, comorbidities, worry about getting infected, perceived COVID-19 impact, belief regarding vaccine efficacy, positive attitude towards mandatory measures, and vaccine availability were found to be associated with vaccine acceptance across countries. CONCLUSION: Nearabout two-third of the respondants were willing to take COVID-19 vaccine in the four South Asia countries.

Factors Affecting Willingness to Receive COVID-19 Vaccine Among Adults: A Cross-sectional Study in Bangladesh

The COVID-19 vaccine has been made available for emergency use in Bangladesh. However, willingness to receive the vaccine may be affected by varying factors across the country. Therefore, this study aimed to investigate the factors that influence willingness to receive the vaccine among Bangladeshi adults. A population-based cross-sectional online survey was conducted among a sample of 1,725 Bangladesh adults (age 18 years and older). The statistical analysis included univariate, bivariate and multivariate regression model. Findings show that 85% (n = 1463) of respondents were willing to receive the vaccine. Respondents with 1–2 children (aOR: 1.77, 95% CI: 1.00–3.13, P =. 048), perceived risk of being infected (aOR: 1.48, 95% CI: 1.03–2.14, P =. 03), perceived impact on daily life (aOR: 2.53, 95%CI: 1.45–4.44, P =. 001), history of co-morbidities (aOR: 2.04, 95% CI: 1.37–3.04, P <. 01), price of the vaccine (aOR: 3.58, 95% CI: 2.34–5.47), physician’s recommendation to receive vaccine (aOR: 2.06, 95% CI: 1.38–3.06, P <. 01), vaccines supplied by government (aOR: 2.31, 95% CI: 1.64–3.25, P <. 01) were found to be motivating factors for willingness to receive the vaccine. Findings indicate that willingness to receive the vaccine is likely to be affected by socio-demographic, and health system factors. This should be carefully considered in the rollout of the vaccination plans in Bangladesh. © 2021 SAGE Publications.

Quality of life of COVID-19 recovered patients in Bangladesh.

Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people's physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals from eight divisions of Bangladesh diagnosed and confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) from June 2020 to November 2020. Given a response rate of 60% in a pilot study, a random list of 6400 COVID-19 patients was generated to recruit approximately 3200 patients from eight divisions of Bangladesh and finally a total of 3244 participants could be recruited for the current study. The validated Bangla version of the World Health Organization Quality of Life Brief (WHOQOL-BREF) questionnaire was used to assess the QoL. Data were analyzed by STATA (Version 16.1) and R (Version 4.0.0). All the procedures were conducted following ethical approval and in accordance with the Declaration of Helsinki. The mean scores of QoL were highest for the physical domain (68.25±14.45) followed by social (65.10±15.78), psychological (63.28±15.48), and environmental domain (62.77±13.07). Psychological and physical domain scores among females were significantly lower than the males (p<0.001). The overall quality of life was lower in persons having a chronic disease. Participants over 45 years of age were 52% less likely to enjoy good physical health than the participants aged below 26 years (AOR: 0.48, CI: 0.28-0.82). The quality of life of employed participants was found 1.8 times higher than the unemployed (AOR: 1.80, CI: 1.11-2.91). Those who were admitted to hospitals during infection had a low QoL score in physical, psychological, and socials domains. However, QoL improved in all aspect except the psychological domain for each day passed after the diagnosis. These findings call for a focus on the quality of life of the COVID-19 affected population, with special emphasis given to females, older adults, unemployed, and people with comorbidities.

Acceptance of COVID-19 vaccine and its determinants: evidence from a large sample study in Bangladesh.

AIM: Our study aimed to understand the acceptance level of the COVID-19 vaccine and its determinants among the adult Bangladeshi population. METHODOLOGY: This cross-sectional study was conducted in all eight divisions of Bangladesh. Data from 7,357 adult respondents were collected between January 17 and February 2, 2021, using a self-administered semi-structured questionnaire. Statistical software STATA (Version 16.1) was used for all analyses. RESULTS: The majority of study participants were from the Dhaka division (34.24%). The most common age group was ≤30 years (46.18%). Almost two-thirds of respondents were male (65.50%) and married (67.76%). A large portion (79.85%) of people who had positive vaccine intentions believed that vaccination should be made mandatory for everyone. The majority of the respondents thought that the vaccine would work against COVID-19 infection (67%). In the binary logistic regression analysis, participants who had the education level of graduation or above (AOR = 1.80), age ≥50 years (AOR = 1.97), students (AOR = 2.98), monthly income ≥41,000 BDT (AOR = 2.22), being resident of rural area (AOR = 2.24), respondents from Khulna division (AOR = 6.43) were more likely to receive a COVID-19 vaccine. Those who had family members diagnosed with COVID-19 (AOR = 1.24), presence of chronic disease (AOR = 0.72), and those who were vaccinated in the last few years (AOR = 1.32) were also more likely to accept the COVID-19 vaccine. CONCLUSION: Most of the respondents were willing to be vaccinated based on the belief that the vaccine will work against COVID-19. As rumors are generating daily, there is a need for policy-level initiative and evidence-based mass media promotion to keep inspired the general Bangladeshi people to accept the COVID-19 vaccine whenever it will be available at the individual level.

Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide epidemiological emergency, and the risk factors for the multiple waves with new COVID-19 strains are concerning. This study aims to identify the most significant risk factors for spreading COVID-19 to help policymakers take early measures for the next waves. METHODS: We conducted the study on randomly selected 29 countries where the pandemic had a downward trend in the daily active cases curve as of June 10, 2020. We investigated the association with the standardized spreading index and demographical, environmental, socioeconomic, and government intervention. To standardize the spreading index, we accounted for the number of tests and the timeline bias. Furthermore, we performed multiple linear regression to identify the relative importance of the variables. RESULTS: In the correlation analysis, air pollution, PM2.5 (r = 0.37, p = 0.0466), number of days to impose lockdown from first case (r = 0.38, p = 0.0424) and total confirmed cases on the first lockdown (r = 0.61, p = 0.0004) were associated with outcome measures. In the adjusted model, air pollution ( ß 1  = 4.5, p = 0.0127, |t| = 3.1) and overweight prevalence ( ß 1  = 4.7, p = 0.0187, |t| = 2.9) were the most significant exposure variable for spreading of COVID-19. CONCLUSION: Our findings showed that countries with larger PM2.5 values and comparatively more overweight populations are at higher risk of spreading COVID-19. Proper preventive measures may reduce the spreading.

COVID-19 vaccine rumors and conspiracy theories: The need for cognitive inoculation against misinformation to improve vaccine adherence.

INTRODUCTION: Rumors and conspiracy theories, can contribute to vaccine hesitancy. Monitoring online data related to COVID-19 vaccine candidates can track vaccine misinformation in real-time and assist in negating its impact. This study aimed to examine COVID-19 vaccine rumors and conspiracy theories circulating on online platforms, understand their context, and then review interventions to manage this misinformation and increase vaccine acceptance. METHOD: In June 2020, a multi-disciplinary team was formed to review and collect online rumors and conspiracy theories between 31 December 2019-30 November 2020. Sources included Google, Google Fact Check, Facebook, YouTube, Twitter, fact-checking agency websites, and television and newspaper websites. Quantitative data were extracted, entered in an Excel spreadsheet, and analyzed descriptively using the statistical package R version 4.0.3. We conducted a content analysis of the qualitative information from news articles, online reports and blogs and compared with findings from quantitative data. Based on the fact-checking agency ratings, information was categorized as true, false, misleading, or exaggerated. RESULTS: We identified 637 COVID-19 vaccine-related items: 91% were rumors and 9% were conspiracy theories from 52 countries. Of the 578 rumors, 36% were related to vaccine development, availability, and access, 20% related to morbidity and mortality, 8% to safety, efficacy, and acceptance, and the rest were other categories. Of the 637 items, 5% (30/) were true, 83% (528/637) were false, 10% (66/637) were misleading, and 2% (13/637) were exaggerated. CONCLUSIONS: Rumors and conspiracy theories may lead to mistrust contributing to vaccine hesitancy. Tracking COVID-19 vaccine misinformation in real-time and engaging with social media to disseminate correct information could help safeguard the public against misinformation.

A Comparative Study of Classification Approaches for COVID-19 Prediction

The novel coronavirus (COVID-19), a highly infectious disease that first found at Wuhan Province of China in Dec 2019, spread worldwide in some months and already become a pandemic. Covid-19 has already changed the world economic structure, people's religious, political, social life, public health structure, people's daily life structure and also made millions of people jobless. The only way to fight this epidemic is to identify the infected person as soon as possible and separate them from a healthy person, so that they can't infect anyone again. At present, RT-PCR is currently used to detect coronavirus patients around the world. But the World Health Organization (WHO) said that RT-PCR suffers from low sensitivity and low specificity for early-stage cases. Recent research has shown that chest CT scan images play a beneficial role in identifying coronavirus cases. In this study, we compared the performances of four classification algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ET), and Convolutional Neural Network (CNN) for classifying COVID-19 cases and proposed a prediction model based on classification results. The result shows that our proposed CNN model outperformed the other classification algorithms and obtained an accuracy of 98.0%. © 2021 IEEE.

COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis.

Infodemics, often including rumors, stigma, and conspiracy theories, have been common during the COVID-19 pandemic. Monitoring social media data has been identified as the best method for tracking rumors in real time and as a possible way to dispel misinformation and reduce stigma. However, the detection, assessment, and response to rumors, stigma, and conspiracy theories in real time are a challenge. Therefore, we followed and examined COVID-19-related rumors, stigma, and conspiracy theories circulating on online platforms, including fact-checking agency websites, Facebook, Twitter, and online newspapers, and their impacts on public health. Information was extracted between December 31, 2019 and April 5, 2020, and descriptively analyzed. We performed a content analysis of the news articles to compare and contrast data collected from other sources. We identified 2,311 reports of rumors, stigma, and conspiracy theories in 25 languages from 87 countries. Claims were related to illness, transmission and mortality (24%), control measures (21%), treatment and cure (19%), cause of disease including the origin (15%), violence (1%), and miscellaneous (20%). Of the 2,276 reports for which text ratings were available, 1,856 claims were false (82%). Misinformation fueled by rumors, stigma, and conspiracy theories can have potentially serious implications on the individual and community if prioritized over evidence-based guidelines. Health agencies must track misinformation associated with the COVID-19 in real time, and engage local communities and government stakeholders to debunk misinformation.