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The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.
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Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The world is becoming a place where the number of emergencies and humanitarian crises is increasing rapidly due to economic inequality and the gap between developed and underdeveloped countries, as well as climate changes leading to disruption of the natural balance and development of natural disasters. The most vulnerable groups of the population including women and children always are affected by disasters. The younger the child, the more vulnerable he/she is, especially if not naturally fed or having a mother or parents. Various humanitarian organizations have been involved in a number of crises, with the World Health Organization and UNICEF and other United Nations-related organizations leading the way. In the care of mothers, infants and young children, most important is to ensure appropriate nutrition because otherwise it can result in life-threatening health conditions. The lack of protection, support and promotion of natural nutrition (breastfeeding) and its disruption and undermining by uncritical and uncontrolled donations and distribution of infant formula are the biggest challenge due to the lack of information of mothers, those who provide support in emergencies from both governmental and non-governmental sector, without cross-sectoral cooperation, thus causing uncoordinated and sometimes harmful interventions. Therefore, it is recommended that governments issue guidelines on infant and young child nutrition prior to the occurrence of an emergency, and crisis management regulations in which the issue of infant and young child nutrition will be given due consideration.Copyright © 2020 Croatian Paediatric Society. All rights reserved.
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Background: Tocilizumab, an interleukin-6 (IL-6) antagonist, is being evaluated for the management of covid-19 pneumonia. The objective of this study was to assess the effectiveness of Tocilizumab in severe covid-19 pneumonia. Method(s): This was a retrospective, observational, single centre study performed in 121 patients diagnosed with severe covid-19 pneumonia. 83 patients received standard of care treatment whereas 38 patients received tocilizumab along with standard of care. Tocilizumab was administered intravenously at 8mg/kg (upto a maximum of 800mg). The second dose of Tocilizumab was given 12 to 24 hours apart. The primary outcome measure was ICU related and hospital related mortality. The secondary outcome measures were change in clinical status of patients measured by WHO (World Health Organisation) 7 category ordinary scale, changes in interleukin-6 (IL-6) levels, secondary infections and duration of ICU stay. Result(s): Tocilizumab was administered between 3-27 days after the patient reported symptoms ( a median of 10.9 days ) and between the 1st to 3rd day of ICU admission (median of 2.1 days) . In Tocilizumab group, 16(42.1%) of 38 patients died in ICU whereas in standard of care group, 27(32.53%) of 83 patients died. The difference in clinical status assessed using WHO (World Health Organisation) 7 category ordinary scale at 28 days between Tocilizumab group and standard of care group was not statistically significant (odds ratio 1.35, 95% confidence interval 0.61 to 2.97, p = 0.44). Conclusion(s): Tocilizumab plus standard care was not superior to standard care alone in reducing mortality and improving clinical outcomes at day 28.Copyright © RJPT All right reserved.
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The COVID-19 pandemic has caused a myriad of socio-economic challenges spanning from job losses to food shortages in cities and towns because of imposed lockdowns. It has affected agricultural value chains across the world, but little has been done to quantify the impacts and determine the implications for policy and strategy frameworks. The extent to which COVID-19 has affected the traders and vendors of horticultural produce in a developing city remains unknown. This study applies the multi-methods approach to explore the impact COVID-19 has had on the vendors and traders of horticultural produce, with a focus on the city of Masvingo. Data was collected using semi-structured questionnaire survey administered amongst vendors and traders in Masvingo city, key informant interviews and document analysis. The results show that COVID-19 disrupted the horticultural value chains. The major impact was felt on the inbound and outbound logistics. This had consequent undesirable effects on vendors and traders' livelihoods exacerbated by COVID-19‘s social, economic and psychological knock-on effects which aggravated poverty and suffering amongst horticultural vendors and traders. The study observes the need for policy and strategic interventions to build robust value chain capacity for horticultural produce in a comprehensive manner. This would help to address the plight of the players involved and abrogate the spill-over effects associated with extreme vulnerability to COVID-19-induced poverty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.
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Objective: To determine the diagnostic accuracy of elevated C reactive protein (CRP) and ferritin in predicting severe Covid-19 infection using the World Health Organization's (WHO) Covid-19 severity classification as gold standard. Study Design: Descriptive study. Place and Duration of Study: This study was conducted at the Pak Emirates Military Hospital, Rawalpindi, from January 1st 2021 till April 30th 2021. Ethical review committee's (ERC) approval was taken and good clinical practice guidelines were followed. Material(s) and Method(s): Baseline blood samples were sent to the hospital laboratory for the measurement of C reactive protein and ferritin levels. PCR was taken as gold standard for the diagnosis of Corona virus disease. Patients were classified into severe and non-severe categories using WHO classification of severity. Sensitivity, specificity, diagnostic accuracy, negative predictive value and positive predictive value were calculated for elevated CRP and ferritin. Result(s): There were 65 (57.5%) patients who had severe Covid-19 disease and 48 (42.5%) patients who had non-severe Covid-19 disease. Among the patients with severe Covid-19, 57 (87.7%) had elevated CRP levels, and 50 (76.9%) patients had elevated ferritin levels. Testing ferritin levels, against the severity of Covid-19 patients, there was a sensitivity of 76.9%, specificity of 79.2%, positive predictive value (PPV) of 83.3%, negative predictive value (NPV) of 71.7% and diagnostic accuracy of 77.8%. Testing CRP levels, there was a sensitivity of 87.7%, specificity of 85.4%, PPV of 89.1%, NPV of 83.6% and diagnostic accuracy of 86.7%. Conclusion(s): The results from our study show that CRP has a slightly improved diagnostic accuracy as compared to ferritin. However, both these markers have value in the prediction of severity of Covid-19 infection.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.
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INTRO: Viruses, including SARS-CoV-2, which causes COVID-19, are constantly changing. These genetic changes (aka mutations) occur over time and can lead to the emergence of new variants that may have different characteristics. After the first SARS-CoV-2 genome was published in early 2020, scientists all over the world soon realized the immediate need to obtain as much genetic information from as many strains as possible. However, understanding the functional significance of the mutations harbored by a variant is important to assess its impact on transmissibility, disease severity, immune escape, and the effectiveness of vaccines and therapeutics. METHODS: Here in Canada, we have developed an interactive framework for visualizing and reporting mutations in SARS-CoV-2 variants. This framework is composed of three stand-alone yet connected components;an interactive visualization (COVID-MVP), a manually curated functional annotation database (pokay), and a genomic analysis workflow (nf-ncov-voc). Findings: COVID-MVP provides (i) an interactive heatmap to visualize and compare mutations in SARS-CoV-2 lineages classified across different VOCs, VOIs, and VUMs;(ii) mutation profiles including the type, impact, and contextual information;(iii) annotation of biological impacts for mutations where functional data is available in the literature;(iv) summarized information for each variant and/or lineage in the form of a surveillance report;and (v) the ability to upload raw genomic sequence(s) for rapid processing and annotating for real-time classification. DISCUSSION: This comprehensive comparison allows microbiologists and public health practitioners to better predict how the mutations in emerging variants will impact factors such as infection severity, vaccine resistance, hospitalization rates, etc. CONCLUSION: This framework is cloud-compatible & standalone, which makes it easier to integrate into other genomic surveillance tools as well. COVID-MVP is integrated into the Canadian VirusSeq data portal (https://virusseqdataportal.ca) - a national data hub for SARS-COV-2 genomic data. COVID-MVP is also used by the CanCOGeN and CoVaRR networks in national COVID-19 genomic surveillance.
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On March 11, 2020, the World Health Organization declared the novel coronavirus (COVID-19) outbreak a global pandemic. In April 2020, the Pregnancy and Infant Linked Outcomes Team (PILOT) was established within the Centers for Disease Control and Prevention's (CDC) COVID-19 response structure, specifically to focus on better understanding the impact of COVID-19 in pregnancy. A total of 71 CDC staff deployed to PILOT, collectively contributing more than 99,000 hours to the response over the course of the team's 25-month activation. PILOT led or collaborated on the publication of over 40 manuscripts, managed several clinical guidance documents, and coordinated and provided subject matter expertise to three funded research studies with academic partners. The team developed six CDC webpages, a toolkit for pregnant people and new parents, and disseminated scientific findings with over 350 social media posts on Facebook, Twitter, Snapchat, LinkedIn, and Instagram with nearly 77 million total impressions. In this, we will summarize the work of PILOT, and other parts of the CDC COVID-19 response, including teams focused on vaccine effectiveness and safety, and surveillance and research activities outside of the CDC. We will review several key contributions to our understanding of COVID-19 in pregnancy: (1) pregnant people are at greater risk of severe illness from COVID-19, including hospitalization, admission to an intensive care unit, and the need for mechanical ventilation, compared with nonpregnant women of reproductive age;(2) pregnant people with COVID-19 are more likely to experience complications that can affect their pregnancy and developing baby, including stillbirth and preterm delivery, compared to pregnant people without COVID-19;(3) there are no recognized maternal or fetal adverse effects of COVID-19 vaccines in pregnancy;and (4) COVID-19 vaccine during pregnancy is effective in preventing severe illness, hospitalization and death among pregnant people, as well as preventing severe illness in infants up to age six months.
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The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.
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Background: The new novel Coronavirus 2019 (nCOV-19 or COVID-19) has caused an unprecedented pandemic in humans. All nations have heightened their surveillances after the quick diagnosis of potential cases of the COVID-19. Objective(s): Recent statistics have mentioned that virus outbreak in tropical countries is relatively low compared to cold nations. To support this conclusion, we considered the six main tropical regions to investigate the pandemic distribution at the initial phase. Method(s): Chi-square test was applied to understand the correlation between outbreak and temperature changes. Significant probability P-value was set to P<0.01. P-values were calculated to both positive and death cases. Result(s): Out of 1211562 infected cases, 41776 cases (3.45%) were registered at hightemperature countries (P<0.0001) and 1161786 cases (96.55%) at other countries like European countries or the USA. Moreover, only 1433 mortality cases (2.2%) happened, and the remaining 97.8% of mortality happened among other nations. Conclusion(s): Similar to other respiratory viruses like flu and influenza, there is a low outbreak of COVID-19 in tropical nations compared to the other countries. Apart from weather conditions, it is also recommended to follow the serious preventive measures imposed by governments to survive this novel epidemic.Copyright © 2020
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Objectives: To describe the use of economic evaluation to update the antigens dispensed by the Colombian Expanded Program on Immunization (EPI) from 2000 and 2021. Method(s): a review of economic evaluation of vaccines (EEV) studies conducted by the Expanded Program of Immunization in Colombia between 2000 and 2021. A literature search was carried out in different databases complemented with information obtained from different stakeholders who participated in the updating process. Result(s): In 2000, sponsored by the Pan-American Health Office of the World Health Organization (PAHO/WHO), was conducted the cost-effectiveness analysis of vaccination against Hemophilus influenzae type b was the first economic evaluation of vaccines (EEV) conducted ever in Colombia. Between 2005 and 2007, 4 EEV (Rotavirus, Heptavalent Pneumococcus, Influenza and Hepatitis A) were carried out in order to inform the decision process at local level in Bogota DC, the Colombian capital. Between 2007 and 2010, the Ministry of Health sponsored 8 EEV (Rotavirus, 7- and 10-valent pneumococcus, Influenza, Hepatitis A, chickenpox, tetanus in men, and HPV) which were used to decide about the introduction of new vaccines at national level. Subsequently, with the support of PAHO's PROVAC initiative, Colombia went from having 6 EPI vaccines in the 1990s, to 21 EPI vaccines that currently protect against 29 diseases, not including the vaccines used against COVID-19 which Colombia have been using since March 2021. Conclusion(s): Colombia has been one of the middle-income countries with the highest number of vaccines included in its EPI in the last 20 years and the use of the EEV has been essential for decision-making.Copyright © 2023
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Objectives: To describe the mortality from diabetes mellitus before and after the first year of the COVID 19 pandemic in Colombia. Method(s): We conducted an ecological study to describe mortality from DM in Colombia by sex and age groups, before and in the first year of the pandemic in Colombia. We obtained DM mortality data from the national agency for population statistics (known as DANE for its initials in Spanish) which collects vital statistics in Colombia. We analyzed anonymized mortality records coded as DM (code 601 from causes of mortality grouped according to the list 6/67 of the PAHO for ICD, 10th revision) for 2019-2020 considering only the underlying cause of death. The variables considered were year of registration of death, number of deaths per year, sex, age, and underlying cause of death. Result(s): In 2019 and 2020 there were 244,355 and 300,853 deaths by all causes respectively. Out of 56,498 (23.1%) excess deaths, 46,019 were due to COVID-19 (code U071). Deaths from DM for were 7,967 (3.26% out of total;2.71% men - 3.94% women) in 2019 and 10,198 (3.39% out of total;2.82% men - 4.15% women) in 2020. The increase for deaths from DM was 28% (n=2,231) 32.7% for men(n=1,193) and 24% for women (n=1,038). Conclusion(s): The COVID-19 pandemic increased deaths from DM in Colombia with a greater impact on men. Priority should be given to rebuild health care services for chronic diseases.Copyright © 2023