https://escholarship.org/content/qt8pq8z2jf/qt8pq8z2jf.pdf?t=qs5r88

Traditional Chinese Medicine (TCM) in the treatment of COVID-19 and other viral infections: Efficacies and mechanisms.

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

/pdf/a-performance-comparison-of-supervised-machine-learning-1rt40b4tdw.pdf

A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.

Background After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. Methods We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. Findings Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. Interpretation States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

/pdf/factors-affecting-covid-19-infected-and-death-rates-inform-xritedep09.pdf

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.

Coronaviruses are a large family of viruses that are known to infect both humans and animals. However, the evidence of inter-transmission of coronavirus between humans and companion animals is still a debatable issue. There is substantial evidence that the virus outbreak is fueled by zoonotic transmission because this new virus belongs to the same family of viruses as SARS-CoV associated with civet cats, and MERS-CoV associated with dromedary camels. While the whole world is investigating the possibility about the transmission of this virus, the transmission among humans is established, but the interface between humans and animals is not much evident. Not only are the lives of human beings at risk, but there is an equal potential threat to the animal world. With multiple reports claiming about much possibility of transmission of COVID-19 from humans to animals, there has been a significant increase in the number of pets being abandoned by their owners. Additionally, the risk of reverse transmission of COVID-19 virus from companion pets like cats and dogs at home is yet another area of concern. The present article highlights different evidence of human-animal interface and necessitates the precautionary measures required to combat with the consequences of this interface. The Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have suggested various ways to promote awareness and corroborate practices for helping people as well as animals to stay secure and healthy.

/pdf/human-animal-interface-of-sars-cov-2-covid-19-transmission-a-3trjobux0h.pdf

Human animal interface of SARS-CoV-2 (COVID-19) transmission: a critical appraisal of scientific evidence.

The study aims to examine the CO2 emissions by considering the implication of COVID-19 under strict lockdown in India. The nonlinear (asymmetric) relationship is investigated between CO2 emission and COVID-19 with its specific determinants. The positive and negative asymmetries of COVID-19 determinants are also captured by using econometric techniques. The daily data series of CO2 emission, new confirmed cases, confirmed deaths, and lockdown as dummy variables from January 30, 2020, to December 1, 2020, for India is analyzed by employing the nonlinear autoregressive distributed lag model. This research revealed a significant nonlinear relationship between CO2 emission and COVID-19. The bound test and asymmetric coefficients are confirmed by the variables' long- and short-run relationships. The dynamic multiplier graphs present that India's strict lockdown due to the rapid increase in COVID-19 patients significantly reduces toxic gas emissions, especially CO2 emissions. This asymmetric relationship has been proficiently declared that unhealthy public routine, extra traffic, and unhygienic gases released in the air become the reason for environmental destruction. The lockdown is practically imposed for specific periods and reasons, contributing to reducing toxic emissions, but it is not a permanent solution for environmental sustainability. The government of India, policymakers, and environmentalists should make people aware of unhealthy and environmentally envying activities and policies and long-term applicable strategies should be designed to upgrade the environment's quality.

/pdf/modeling-the-relationship-between-carbon-emissions-and-18qj65hi5c.pdf

Modeling the relationship between carbon emissions and environmental sustainability during COVID-19: a new evidence from asymmetric ARDL cointegration approach.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued “A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)”; now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis (including agents and Traditional Chinese Medicine (TCM) agents), diagnosis (including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest x-ray, and CT features of asymptomatic infections), treatments (including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, qingfei paidu decoction, lianhua qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)), and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients.

https://link.springer.com/content/pdf/10.1186/s40779-020-00270-8.pdf

Yan-Jun Zhong

Papers

Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19: An evidence-based clinical practice guideline (updated version)