Understanding Human-COVID-19 Dynamics using Geospatial Big Data: A Systematic Literature Review (preprint)

The COVID-19 pandemic has changed human life. To mitigate the pandemic's impacts, different regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Consequently, the human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and the pandemic is imperative for mitigating risks from future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research, we conducted a synthesis study based on 67 selected publications between March 2020 and January 2023. We extracted key information from each article across six categories, e.g., research area and time, data, methodological framework, and results and conclusions. Results reveal that regression models were predominant in relationship detection, featured in 67.16% of papers. Only two papers employed spatial-temporal models, notably underrepresented in the existing literature. Studies examining the effects of policies and human mobility on the pandemic's health impacts were the most prevalent, each comprising 12 articles (17.91%). Only 3 papers (4.48%) delved into bidirectional interactions between human responses and the COVID-19 spread. These findings shed light on the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems.

Statistical Machine Learning Meets High-Dimensional Spatiotemporal Challenges -- A Case Study of COVID-19 Modeling (preprint)

Diverse non-pharmacological interventions (NPIs), serving as the primary approach for COVID-19 control prior to pharmaceutical interventions, showed heterogeneous spatiotemporal effects on pandemic management. Investigating the dynamic compounding impacts of NPIs on pandemic spread is imperative. However, the challenges posed by data availability of high-dimensional human behaviors and the complexity of modeling changing and interrelated factors are substantial. To address these challenges, this study analyzed social media data, COVID-19 case rates, Apple mobility data, and the stringency of stay-at-home policies in the United States throughout the year 2020, aiming to (1) uncover the spatiotemporal variations in NPIs during the COVID-19 pandemic utilizing geospatial big data; (2) develop a statistical machine learning model that incorporates spatiotemporal dependencies and temporal lag effects for the detection of relationships; (3) dissect the impacts of NPIs on the pandemic across space and time. Three indices were computed based on Twitter (currently known as X) data: the Negative and Positive Sentiments Adjusted by Demographics (N-SAD and P-SAD) and the Ratio Adjusted by Demographics (RAD), representing negative sentiment, positive sentiment, and public awareness of COVID-19, respectively. The Multivariate Bayesian Structural Time Series Time Lagged model (MBSTS-TL) was proposed to investigate the effects of NPIs, accounting for spatial dependencies and temporal lag effects. The developed MBSTS-TL model exhibited a high degree of accuracy. Determinants of COVID-19 health impacts transitioned from an emphasis on human mobility during the initial outbreak period to a combination of human mobility and stay-at-home policies during the rapid spread phase, and ultimately to the compound of human mobility, stay-at-home policies, and public awareness of COVID-19.

Inference on spatiotemporal dynamics for networks of biological populations (preprint)

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference due to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been developed which enables effective likelihood-based inference for the high-dimensional partially observed stochastic dynamic models arising in metapopulation systems. The COVID-19 pandemic provides a situation where mathematical models and their policy implications were widely visible, and we use the new inferential technology to revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model limitations, and leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on January 23, 2020 in China was more effective than previously thought. We proceed to recommend statistical analysis standards for future metapopulation system modeling.

Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): A retrospective study (preprint)

Background With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop a practical predictive model for mortality among patients infected with new COVID-19 variants.Methods Demographic, clinical, and laboratory data of COVID-19 patients were retrospectively collected at our hospital between December 22, 2022, and February 15, 2023. Logistic regression (LR), decision tree (DT), and Extreme Gradient Boosting (XGBoost) models were developed to predict mortality. Those models were separately visualized via nomogram, decision trees, and Shapley Additive Explanations (SHAP). To evaluate those models, accuracy, sensitivity, specificity, precision, Youden’s index, and area under curve (AUC, 95% CI) were calculated.Results A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Noninvasive ventilation, intubation, myoglobin, INR, age, number of diagnoses, respiratory, pulse, neutrophil, and albumin were the most important predictors of mortality among new COVID-19 variants. The AUC of LR, DT, and XGBoost models were 0.959, 0.878, and 0.961, respectively. The diagnostic accuracy was 0.926 for LR, 0.913 for DT, and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.983) and specificity (0.940).Conclusion Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.

Research progress on palmitoylation of SARS-CoV-2 surface proteins and receptor ACE2

It has been more than 2 years since the outbreak of corona virus disease 2019（COVID-19） caused by severe acute respiratory syndrome coronavirus 2（SARS-CoV-2）.SARS-CoV-2 is a member of positive single-stranded RNA viruses and could infect multiple mammals.Palmitoylation is a post-translational lipid modification of protein, which regulates protein localization and trafficking.Spike protein（S）, envelope protein（E） and SARS-CoV-2 receptor ACE2 have been identified of being palmitoylated.This paper reviews the research progress on the palmitoylation of S, E and ACE2, including the sites of palmitoylation of S protein, the enzymes involved in this process, and their functions.Through the integrated review of these contents, which would provide mechanistic insights into the pathogenesis and treatment of COVID-19.

Survey and enlightenment on the wearing of protective equipment of nurses working on the front line against COVID-19.

ABSTRACT: At present, coronavirus disease 2019 (COVID-19) remains a significant challenge for health workers around the world. This survey aims to highlight the status of the implementation of occupational protection measures for nurses working on the front line against COVID-19, and to analyze the problems in the process of wearing protective equipment.This cross-sectional study was conducted among 165 nurses who worked in COVID-19-stricken areas in China in March 2020. The questionnaire covered 3 aspects, namely: general information, the current condition of protective equipment wearing, and the wearing experience of protective equipment.A total of 160 (96.97%) valid questionnaires were collected. The average time of wearing protective equipment for the nurses surveyed was 6.38 ±â€Š3.30 hours per working day. For first-line nurses with low risk of infection, repeated wear of protective equipment was as follows: medical protective mask 30.77%, double latex gloves 8.46%, goggles/protective mask 15.38%, protective suit 15.38%; less wear of protective equipment were as follows: work cap 7.69%, surgical mask 7.69%, single layer latex gloves 30.77%, goggles/protective mask 30.77%, and isolation gown 46.15%. For nurses who were at moderate risk of infection, repeated wear of protective equipment was as follows: surgical mask 62.22%, goggles/protective mask 68.89%, and isolation gown 65.56%; less wear: work cap 3.33%, medical protective mask 15.56%, latex gloves 15.56%, goggles/protective mask 5.56%, and protective suit 16.67%. For front-line nurses with high risk of infection, repeated wear of protective equipment was as follows: surgical mask 64.91%, more than double latex gloves 8.77%, goggles/protective mask 75.44%, isolation gown 75.44%; less wear: work cap 1.75%, medical protective mask 1.75%, latex gloves 26.32%, goggles/ protective mask 1.75%, protective suit 1.75%. The main discomforts of wearing protective equipment were poor vision due to fogging (81.88%), stuffiness (79.38%), poor mobility (74.38%), sweating (72.5%), and skin damage (61.25%).More detailed personal protection standards should be developed, and the work load of nurses should be reduced. Actions should be taken to ensure the scientific implementation of personal protective measures. To solve practical clinical problems, future protective equipment may focus on the research and development of protective equipment applicable for different risk levels, as well as the research on integrated design, fabric innovation, and reusability.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.

Objective: Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China. Methods: Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods. Results: From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (ß2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447). Conclusions: The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Association between diagnostic delay and prognosis of pulmonary tuberculosis in Shandong, China: a retrospective study.

BACKGROUND: Tuberculosis (TB) is one of the main infectious diseases that seriously threatens global health, while diagnostic delay (DD) and treatment dramatically threaten TB control. METHODS: Between 2005 and 2017 in Shandong, China, we enrolled pulmonary tuberculosis (PTB) patients with DD. DD trends were evaluated by Joinpoint regression, and associations between PTB patient characteristics and DD were estimated by univariate and multivariate logistic regression. The influence of DD duration on prognosis and sputum smear results were assessed by Spearman correlation coefficients. RESULTS: We identified 208,822 PTB cases with a median DD of 33 days (interquartile range (IQR) 18-63). The trend of PTB with DD declined significantly between 2009 and 2017 (annual percent change (APC): - 4.0%, P = 0.047, 2009-2013; APC: - 6.6%, P = 0.001, 2013-2017). Patients aged > 45 years old (adjusted odds ratio (aOR): 1.223, 95% confidence interval (CI) 1.189-1.257, 46-65 years; aOR: 1.306, 95% CI 1.267-1.346, > 65 years), farmers (aOR: 1.520, 95% CI 1.447-1.596), and those with a previous treatment history (aOR: 1.759, 95% CI 1.699-1.821) were prone to developing long DD (> 30 days, P < 0.05). An unfavorable outcome was negatively associated with a short DD (OR: 0.876, 95% CI 0.843-0.910, P < 0.001). Sputum smear positive rate and unfavorable outcomes were positively correlated with DD duration (Spearman correlation coefficients (rs) = 1, P < 0.001). CONCLUSIONS: The DD situation remains serious; more efficient and comprehensive strategies are urgently required to minimize DD, especially for high-risk patients.

The Emerging Role of RNA Modifications in the Regulation of Antiviral Innate Immunity.

Posttranscriptional modifications have been implicated in regulation of nearly all biological aspects of cellular RNAs, from stability, translation, splicing, nuclear export to localization. Chemical modifications also have been revealed for virus derived RNAs several decades before, along with the potential of their regulatory roles in virus infection. Due to the dynamic changes of RNA modifications during virus infection, illustrating the mechanisms of RNA epigenetic regulations remains a challenge. Nevertheless, many studies have indicated that these RNA epigenetic marks may directly regulate virus infection through antiviral innate immune responses. The present review summarizes the impacts of important epigenetic marks on viral RNAs, including N6-methyladenosine (m6A), 5-methylcytidine (m5C), 2'-O-methylation (2'-O-Methyl), and a few uncanonical nucleotides (A-to-I editing, pseudouridine), on antiviral innate immunity and relevant signaling pathways, while highlighting the significance of antiviral innate immune responses during virus infection.

Epidemic spreading with migration in networked metapopulation.

Migration plays a crucial role in epidemic spreading, and its dynamic can be studied by metapopulation model. Instead of the uniform mixing hypothesis, we adopt networked metapopulation to build the model of the epidemic spreading and the individuals' migration. In these populations, individuals are connected by contact network and populations are coupled by individuals migration. With the network mean-field and the gravity law of migration, we establish the N-seat intertwined SIR model and obtain its basic reproduction number ℛ 0 . Meanwhile, we devise a non-markov Node-Search algorithm for model statistical simulations. Through the static network migration ansatz and ℛ 0 formula, we discover that migration will not directly increase the epidemic replication capacity. But when ℛ 0 > 1 , the migration will make the susceptive population evolve from metastable state (disease-free equilibrium) to stable state (endemic equilibrium), and then increase the influence area of epidemic. Re-evoluting the epidemic outbreak in Wuhan, top 94 cities empirical data validate the above mechanism. In addition, we estimate that the positive anti-epidemic measures taken by the Chinese government may have reduced 4 million cases at least during the first wave of COVID-19, which means those measures, such as the epidemiological investigation, nucleic acid detection in medium-high risk areas and isolation of confirmed cases, also play a significant role in preventing epidemic spreading after travel restriction between cities.

Risk Culture and COVID-19 Protective Behaviors: A Cross-Sectional Survey of Residents in China.

The COVID-19 outbreak caused by the Severe Acute Respiratory Syndrome CoronaVirus type 2 (SARS-CoV-2) has spread across the world. However, our understanding of the public responses, in particular in adopting protective behaviors, has been limited. The current study aimed to determine the level of protective behaviors adopted by the residents in China and its association with their cultural attributes. A national cross-sectional online survey was conducted in mainland China from 4th to 13th August 2020. Protective behaviors were assessed as a summed score (ranging from 0 to 40) measured by ten items. The self-report tendency of study participants toward the four cultural attributes (individualism, egalitarianism, fatalism, hierarchy) was rated on a seven-point Likert scale. A total of 17651 respondents returned a valid questionnaire, representing 47.9% of those who accessed the online survey. Most (89.8%) respondents aged between 18 and 45 years in the age range of and 47.7% were male. High levels of protective behaviors (34.04 ± 5.78) were reported. The respondents had high scores in the cultural attributes of hierarchy (Median = 5) and egalitarianism (Median = 5), compared with low scores in individualism (Median = 1) and fatalism (Median = 1). High levels of protective behaviors were associated a higher tendency toward egalitarianism (AOR = 2.90, 95% CI 2.67-3.15) and hierarchy (AOR = 1.66, 95% CI 1.53-1.81) and a low tendency toward fatalism (AOR = 1.79, 95% CI 1.63-1.97) and individualism (AOR = 2.62, 95% CI 2.41-2.85). The cultural attributes explained 17.3% of the variations in the protective behavioral scores. In conclusion, the adoption of protective behaviors is associated a risk culture characterized by high levels of hierarchy and egalitarianism and low levels of individualism and fatalism. Government actions and communication strategies need to adapt to the cultural characteristics of their target audience.

COVID-19 and Tuberculosis Coinfection: An Overview of Case Reports/Case Series and Meta-Analysis.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases. Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro. Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5-16.73] vs. 8.015 [4.8-8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively. Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

Prevalence and Factors Associated With Burnout of Frontline Healthcare Workers in Fighting Against the COVID-19 Pandemic: Evidence From China.

OBJECTIVES: The sudden outbreak of the novel coronavirus disease (COVID-19) plunged healthcare workers (HCWs) into warfare. This study aimed to determine the prevalence of burnout and the factors associated with it among frontline HCWs fighting COVID-19. METHODS: A cross-sectional survey was conducted among frontline HCWs fighting against the COVID-19 in Wuhan, Harbin, and Shenzhen during the period from February 18 to March 4. Finally, HCWs were recruited using cluster sampling, 1,163 HCWs were included in the final analysis. Burnout was measured using a 22-item Maslach Burnout Inventory scale (MBI scale). RESULTS: Of the participants, 48.6% suffered from burnout, and 21.8% showed a high degree of burnout. Doctors (b = 3.954, P = 0.011) and nurses (b = 3.067, P = 0.042) showed higher emotional exhaustion (EE) than administrators. Participants who worked continuously for more than 8 h a day (b = 3.392, P = 0.000), those who were unable to eat three regular daily meals (b = 2.225, P = 0.008), whose daily water intake was no more than 800 ml (b = 3.007, P = 0.000), who slept for no more than 6 h (b = 1.609, P = 0.036), and who were infected or had colleagues who were infected with COVID-19 (b = 4.182, P = 0.000) experienced much higher levels of EE, while those who could adhere to infection control procedures (b = -5.992, P = 0.000), who were satisfied with their hospital's infection control measures(b = -3.709, P = 0.001), and who could receive sufficient psychological crisis intervention (b = -1.588, P = 0.039) reported lower levels of EE. CONCLUSION: The study reveals that burnout is prevalent among frontline HCWs and that the known factors associated with burnout, such as workload, and the factors directly associated with COVID-19, such as having insufficient protection, can affect burnout symptoms in frontline HCWs. Synergized and comprehensive interventions should be targeted at reducing its occurrence among frontline HCWs fighting COVID-19.

Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study.

AIMS: The aim of this study was to apply the Mendelian randomization (MR) design to explore the potential causal association between COVID-19 and the risk of hypertension disorders in pregnancy. METHODS: Our primary genetic instrument comprised 8 single-nucleotide polymorphisms (SNPs) associated with COVID-19 at genome-wide significance. Data on the associations between the SNPs and the risk of hypertension disorders in pregnancy were obtained from study based on a very large cohort of European population. The random-effects inverse-variance weighted method was conducted for the main analyses, with a complementary analysis of the weighted median and MR-Egger approaches. RESULTS: Using IVW, we found that genetically predicted COVID-19 was significantly positively associated with hypertension disorders in pregnancy, with an odds ratio (OR) of 1.111 [95% confidence interval (CI) 1.042-1.184; P = 0.001]. Weighted median regression also showed directionally similar estimates [OR 1.098 (95% CI, 1.013-1.190), P = 0.023]. Both funnel plots and MR-Egger intercepts suggest no directional pleiotropic effects observed. CONCLUSIONS: Our findings provide direct evidence that there is a shared genetic predisposition so that patients infected with COVID-19 may be causally associated with increased risk of hypertension disorders in pregnancy.

Management strategies in a thoracic surgery ward during COVID-19 pandemic: Experience from West China Hospital.

The coronavirus disease 2019 was first reported in Wuhan in December 2019 and then spread rapidly throughout the world. On March 11, 2020, the World Health Organization declared coronavirus disease 2019 a pandemic. In response to the pandemic, the management division of West China Hospital oversaw the implementation of hospital-wide emergency measures. In accordance with these measures, the hospital's thoracic surgery ward implemented a new management system by reformulating staff training plans, patient admission procedures, and other systems for managing the ward and protecting perioperative patients. Overall, the ward was successful in restoring normal working order, protecting all staff from occupational exposures, and ensuring the safety of inpatients and their families.

The impacts of knowledge, risk perception, emotion and information on citizens' protective behaviors during the outbreak of COVID-19: a cross-sectional study in China.

BACKGROUND: Individual protective behaviors play an important role in the control of the spread of infectious diseases. This study aimed to investigate the adoption of protective behaviors by Chinese citizens amid the COVID-19 outbreak and its associated factors. METHODS: An online cross-sectional survey was conducted from 22 January to 14 February 2020 through Wenjuanxing platform, measuring their knowledge, risk perception, negative emotion, response to official communication, and protective behaviors in relation to COVID-19. A total of 3008 people completed the questionnaire, of which 2845 were valid questionnaires. RESULTS: On average, 71% of respondents embraced protective behaviors. Those who made no error in the knowledge test (AOR = 1.77, p < 0.001) perceived the high severity of the epidemic (AOR = 1.90, p < 0.001), had high negative emotion (AOR = 1.36, p = 0.005), reported good health (AOR = 1.94, p < 0.001), paid high attention to the governmental media (AOR = 4.16, p < 0.001) and trusted the governmental media (AOR = 1.97, p < 0.001) were more likely to embrace protective behaviors after adjustments for variations in potential confounding factors. Women and older people were also more likely to embrace protective behaviors. No regional or educational differences were found in the adoption of protective behaviors. CONCLUSION: The majority of Chinese citizens embraced protective behaviors. Higher levels of protective behaviors are associated with higher knowledge, perceived severity, negative emotion, and attention to and trust in the official governmental media. Official governmental communication is the largest single predictor of protective behaviors.

Efficacy of Traditional Chinese Medicine on COVID-19: Two Case Reports.

Severe acute respiratory syndrome coronavirus 2-infected pneumonia (novel coronavirus-infected pneumonia [NCIP]) is a new viral illness initially identified in the central Chinese city of Wuhan in December 2019. According to the daily report on the epidemic situation of coronavirus disease (COVID-19) issued by the National Health and Family Planning Commission of the People's Republic of China on March 23, 2020: COVID-19 is highly infectious, causing extremely high incidence of NCIP throughout Wuhan and has spread swiftly to 34 provinces within China and >100 other countries around the world between January and March 2020. Up to August 11, 2020, there have been 89,383 cases diagnosed as coronavirus-infected pneumonia; and 4,696 deaths in China (mortality rate of 5.25%) and worldwide reports have confirmed 19,936,210 cases and 732,499 deaths. These figures have been increasing daily. The treatment of viral conditions is well established within the context of Chinese Medicine. We report 2 successful cases in this study showing the patient's chest computed tomography scans and temperature charts made on before, during, and after treatment to demonstrate proof of the positive benefits achieved. Traditional Chinese Medicine (TCM) demonstrates a positive effect in the treatment for COVID-19. It is highly recommended that TCM be incorporated early on in the treatment schedule for COVID-19.

A systematic review of etiology, epidemiology, clinical manifestations, image findings, and medication of 2019 Corona Virus Disease-19 in Wuhan, China.

BACKGROUND: Unknown origin pneumonia has been furiously spreading since the late of December 2019, subsequently spread to approximately all provinces and areas in China and many countries, which was announced as a Public Health Emergency of International Concern by World Health Organization (WHO). The studies on 2019 Corona Virus Disease-19 (COVID-19) conducted from various fields around the world. Herein, the objective of the present study is to summarize the etiology, epidemiology, clinical manifestations, image findings, traceability analysis, and drug development of COVID-19. METHODS: The following electronic databases were searched: Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database, VIP Chinese Science and Technology Periodical Database, and Wanfang Data. Other relevant literature will be manually searched as a compliment. We have reviewed etiology, epidemiology, clinical manifestations, image findings, and medication from case reports and retrospective clinical studies relating to COVID-19 published since the outbreak. RESULTS: The coronavirus is closely related to bat coronavirus and pangolin coronavirus. Besides, the infection pathway is confirmed to be the respiratory and digestive systems. The virus indicates person-to-person transmission and some patients present asymptomatic. The elderly have a higher mortality rate. Rapid and sensitive nucleic acid testing is usually used as a basis for diagnosis. Currently, there is no specific vaccine and antiviral drug. Intervention actions such as travel bans and quarantine adopted have effectively reduced the spread of the epidemic. CONCLUSION: This systemic review will provide high-quality evidence to summarize etiology, epidemiology, clinical manifestations, image findings, traceability analysis, drug development in patients with COVID-19.

How to Understand "Herd Immunity" in COVID-19 Pandemic.

The COVID-19 pandemic has been a global threat. Through rapid and effective surveillance and control, the newly confirmed patients have been fluctuated at a very low level and imported case explained most of them through March, 2020 to the present, indicating China's response has achieved a stage victory. By contrast, the epidemic of COVID-19 in other countries out of China is bursting. Different countries are adopting varied response strategy in terms of their public health system to prevent the spread. Herd immunity has been a hot topic since the outbreak of COVID-19 pandemic. Can it be a possible strategy to combat COVID-19? To fully interpret the knowledge regarding the term upon the background of COVID-19-related health crisis, we aim to systematically review the definition, describe the effective measures of acquiring herd immunity, and discuss its feasibility in COVID-19 prevention. Findings from this review would promote and strengthen the international cooperation and joint efforts when confronting with COVID-19.

[Investigation of modulating effect of Qingfei Paidu Decoction on host metabolism and gut microbiome in rats].

This study is to explore the effect of Qingfei Paidu Decoction(QPD) on the host metabolism and gut microbiome of rats with metabolomics and 16 S rDNA sequencing. Based on 16 S rDNA sequencing of gut microbiome and metabolomics(GC-MS and LC-MS/MS), we systematically studied the serum metabolites profile and gut microbiota composition of rats treated with QPD for continued 5 days by oral gavage. A total of 23 and 43 differential metabolites were identified based on QPD with GC-MS and LC-MS/MS, respectively. The involved metabolic pathways of these differential metabolites included glycerophospholipid metabolism, linoleic acid metabolism, TCA cycle and pyruvate metabolism. Meanwhile, we found that QPD significantly regulated the composition of gut microbiota in rats, such as enriched Romboutsia, Turicibacter, and Clostridium_sensu_stricto_1, and decreased norank_f_Lachnospiraceae. Our current study indicated that short-term intervention of QPD could significantly regulate the host metabolism and gut microbiota composition of rats dose-dependently, suggesting that the clinical efficacy of QPD may be related with the regulation on host metabolism and gut microbiome.