Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Analysis of the characteristics of imported COVID-19 cases in mainland China

Objective This study aimed to examine the epidemic characteristics of the COVID-19 imported cases entering mainland China from March 4, 2020 to October 31, 2021, so as to provide the reference for the prevention and control of imported epidemic at present. Methods Data were collected from the Daily Summary on the COVID-19 epidemic issued by the national/provincial health commission official website from March 4, 2020 to October 31, 2021, including " number of imported cases and existing imported cases and source country/territory and destination province for imported cases. Joinpoint regression was used to examine the time trends in the number of imported cases over time. Results From March 4, 2020 to November 3, 2021, the number of monthly newly imported cases and existing confirmed cases changed as a " W” shape. The imported cases came from 152 counties and territories in total, mainly from Myanmar, United States, Philippines and Russia (accounting for 27.6% of all imported cases). The number of imported cases mainly entered Shanghai, Guangdong, Yunnan, Sichuan, and Fujian, explaining 70.59% of total imported cases. Conclusions The great fluctuating change of imported cases in the mainland of China may be related to the change of global COVID-19 epidemic and domestic prevention and control policies. Considering the imbalanced distribution of source country/territory and destination province of imported cases, the government should take targeted measures in important source countries/terriories and destination provinces. Each province and municipality should modify its policy for preventing the imported epidemic dynamically according to the latest characteristic of source country/territory and virus mutation. © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Analysis of the characteristics of imported COVID-19 cases in mainland China.

Objective This study aimed to examine the epidemic characteristics of the COVID-19 imported cases entering mainland China from March 4, 2020 to October 31, 2021, so as to provide the reference for the prevention and control of imported epidemic at present. Methods Data were collected from the Daily Summary on the COVID-19 epidemic issued by the national/provincial health commission official website from March 4, 2020 to October 31, 2021, including " number of imported cases and existing imported cases and source country/territory and destination province for imported cases. Joinpoint regression was used to examine the time trends in the number of imported cases over time. Results From March 4, 2020 to November 3, 2021, the number of monthly newly imported cases and existing confirmed cases changed as a " W" shape. The imported cases came from 152 counties and territories in total, mainly from Myanmar, United States, Philippines and Russia (accounting for 27.6% of all imported cases). The number of imported cases mainly entered Shanghai, Guangdong, Yunnan, Sichuan, and Fujian, explaining 70.59% of total imported cases. Conclusions The great fluctuating change of imported cases in the mainland of China may be related to the change of global COVID-19 epidemic and domestic prevention and control policies. Considering the imbalanced distribution of source country/territory and destination province of imported cases, the government should take targeted measures in important source countries/terriories and destination provinces. Each province and municipality should modify its policy for preventing the imported epidemic dynamically according to the latest characteristic of source country/territory and virus mutation.Copyright © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Time-varying jumps in China crude oil futures market impacted by COVID-19 pandemic

China crude oil futures market launched in 2018 has become the third-largest global crude oil exchange, indicating its important role in global crude oil markets. Understanding the time-varying jumps of the newly RMB denominated crude oil futures market is not only for the benefit of the market participants in risk management and hedging, but also for the reference of policy-makers in formulating regulation policies, market pricing and financializing energy markets. However, the literature on time-varying jumps in China crude oil futures market is quite scarce compared with existed literature. In this regard, we attempt to study time-varying jumping behaviors of China crude oil futures market impacted by discrete random events, and analyze the sensitivity of jump intensity, jump size and its variance to market volatility and historical volatility, applying the constant and time-varying intensity jump models, based on the daily returns of China crude oil futures market from March 26, 2018 to August 31, 2021. Further, we compare the differential jumps of China crude oil futures market impacted by COVID-19 pandemic. The empirical results have shown that significant time-varying jumping behaviors appear in China crude oil futures market and take on discrete jumping form. The jump intensity is persistent and sensitive to historical volatility of the market. Meanwhile, jump intensity and jump size increase suffered by great public health emergency, and negative jump size arises with high probability. However, the variance of jump size is little sensitive to historical volatility of the market. These findings suggest that the time-varying jumps, especially negative jumps, should be considered for decision-makers and market participants associated with China crude oil futures market. © 2023 Elsevier Ltd

Effect of COVID-19 Pandemic on Acute Coronary Syndrome Clinical Practice Patterns: Findings from a Multicenter Clinician Survey in China

Background: The coronavirus disease 2019 (COVID-19) pandemic has severely affected healthcare systems around the world. This study aimed to investigate the perceptions of cardiologists regarding how the COVID-19 pandemic has affected the clinical practice patterns for acute coronary syndrome (ACS). Method(s): A multicenter clinician survey was sent to 300 cardiologists working in 22 provinces in China. The survey collected demographic information and inquired about their perceptions of how the COVID-19 pandemic has affected ACS clinical practice patterns. Result(s): The survey was completed by 211 (70.3%) cardiologists, 82.5% of whom were employed in tertiary hospitals, and 52.1% reported more than 10 years of clinical cardiology practice. Most respondents observed a reduction in ACS inpatients and outpatients in their hospitals during the pandemic. Only 29.9% of the respondents had access to a dedicated catheter room for the treatment of COVID-19-positive ACS patients. Most respondents stated that the COVID-19 pandemic had varying degrees of effect on the treatment of acute ST-segment elevation myocardial infarction (STEMI), acute non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Compared with the assumed non-pandemic period, in the designed clinical questions, the selection of coronary interventional therapy for STEMI, NSTEMI, and unstable angina during the COVID-19 pandemic was significantly decreased (all p < 0.05), and the selection of pharmacotherapy was increased (all p < 0.05). The selection of fibrinolytic therapy for STEMI during the pandemic was higher than in the assumed non-pandemic period (p < 0.05). Conclusion(s): The COVID-19 pandemic has profoundly affected ACS clinical practice patterns. The use of invasive therapies significantly decreased during the pandemic period, whereas pharmacotherapy was more often prescribed by the cardiologists. Copyright: © 2022 The Author(s).

Aerosol generation from tear film during non-contact tonometer measurement

Aerosols, generated and expelled during common human physiological activities or medical procedures, become a vital carrier for the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). During non-contact intraocular pressure measurements, aerosols can be produced from the tear film on eyes and potentially convey the SARS-CoV-2 in tears, creating a high risk for eye care practitioners and patients. Herein, we numerically investigate deformation and fragmentation of the tear films with various thicknesses and surface tensions that are impinged by an air jet. Evolution of the tear films manifests several types of breakup mechanisms, including both the bag breakup and ligament breakup of tear film on the eyeball, the ligament breakup of tear film on the eyelid margin, and the sheet breakup near the eyelid margin. The sheet near the eyelid margin is critical for generating large droplets and can be formed only if the jet velocity is high enough and the film is sufficiently thick. A criterion based on Weber number and capillary number is proposed for the breakup of tear film into droplets in which three regions are used to classify the film evolution. Our results indicate that eyes with excessive tears have a greater probability of generating aerosols than eyes under normal conditions. We recommend that enhanced protections should be adopted upon measurement for the patients with watery eyes, and the time interval between two adjacent measurements for the same individual should be also prolonged during the COVID-19 pandemic. Published under an exclusive license by AIP Publishing.

Real-Time Detection of COVID-19 Events From Twitter: A Spatial-Temporally Bursty-Aware Method

In the last two years, the outbreak of COVID-19 has significantly affected human life, society, and the economy worldwide. To prevent people from contracting COVID-19 and mitigate its spread, it is crucial to timely distribute complete, accurate, and up-to-date information about the pandemic to the public. In this article, we propose a spatial-temporally bursty-aware method called STBA for real-time detection of COVID-19 events from Twitter. STBA has three consecutive stages. In the first stage, STBA identifies a set of keywords that represent COVID-19 events according to the spatiotemporally bursty characteristics of words using Ripley's K function. STBA will also filter out tweets that do not contain the keywords to reduce the interference of noise tweets on event detection. In the second stage, STBA uses online density-based spatial clustering of applications with noise clustering to aggregate tweets that describe the same event as much as possible, which provides more information for event identification. In the third stage, STBA further utilizes the temporal bursty characteristic of event location information in the clusters to identify real-world COVID-19 events. Each stage of STBA can be regarded as a noise filter. It gradually filters out COVID-19-related events from noisy tweet streams. To evaluate the performance of STBA, we collected over 116 million Twitter posts from 36 consecutive days (from March 22, 2020 to April 26, 2020) and labeled 501 real events in this dataset. We compared STBA with three state-of-the-art methods, EvenTweet, event detection via microblog cliques (EDMC), and GeoBurst+ in the evaluation. The experimental results suggest that STBA outperforms GeoBurst+ by 13.8%, 12.7%, and 13.3% in terms of precision, recall, and F ₁score. STBA achieved even more improvements compared with EvenTweet and EDMC.

Study on the risk of death of coagulation index in patients with covid-19

Objective: The aim of this study was to elucidate the association of COVID-19 prognosis with the indexes of inflammation and coagulation. Methods: The clinical data of 103 cases of COVID-19 were retrospectively analyzed. COX regression models and 95% confidence intervals (CIs) were applied to estimate the COVID-19 prognosis. Results: The results showed that C-reactive protein (HR=1.007, p<0.001), procalcitonin (HR=1.013, p=0.006), prothrombin time (HR=1.190, p<0.001), Fibrinogen (HR=0.784, p=0.002) and D-dimer (HR=1.078, p<0.001) is associated with an increased risk of COVID-19 death. Among the inflammation indicators, the maximum area under the ROC curve of NLR is 0.87. In the coagulation index, the maximum area under the ROC curve of PT is 0.84. For the combined indicators of inflammation and coagulation, the area under the ROC curve is 0.89. Conclusions: In conclusion, we found that the length of PT at admission and the level of fibrinogen and D-dimer were related to the risk of COVID-19 death. It may be considered to jointly predict the risk of death of COVID-19 with inflammation and coagulation indicators. © 2021 A. CARBONE Editore. All rights reserved.

Establishment of prediction models for COVID-19 patients in different age groups based on Random Forest algorithm.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Age is an independent factor in death from the disease, and predictive models to stratify patients according to their mortality risk are needed. AIM: To compare the laboratory parameters of the younger (≤70) and the elderly (>70) groups, and develop death prediction models for the two groups according to age stratification. DESIGN: A retrospective, single-center observational study. METHODS: This study included 437 hospitalized patients with laboratory-confirmed COVID-19 from Tongji Hospital in Wuhan, China, 2020. Epidemiological information, laboratory data and outcomes were extracted from electronic medical records and compared between elderly patients and younger patients. First, recursive feature elimination (RFE) was used to select the optimal subset. Then, two random forest (RF) algorithms models were built to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses. RESULTS: Comparisons of the laboratory data of the two age groups revealed many different laboratory indicators. RFE was used to select the optimal subset for analysis, from which 11 variables were screened out for the two groups. The RF algorithm were built to predict the prognoses of COVID-19 patients based on the best subset, and the area under ROC curve (AUC) of the two groups is 0.874 (95% CI: 0.833-0.915) and 0.842 (95% CI: 0.765-0.920). CONCLUSION: Two prediction models for COVID-19 were developed in the patients with COVID-19 based on random forest algorithm, which provides a simple tool for the early prediction of COVID-19 mortality.

Pros and Cons of Artificial Intelligence - Lessons from E-Government Services in the COVID-19 Pandemic

How to understand the role and impact of information technology and artificial intelligence has triggered a big debate. To explore the pros and cons of artificial intelligence and its applications, this article takes the face mask distribution programs in the COVID-19 pandemic as research objects, conducting a multi-case comparative study of three cities in China. By manual coding of a total of 4560 We Chat official account messages, and by analyzing information related to the distribution process, it was found that: (1) On the demand side, the task complexity, the demand diversity, and the unstructured decision-making process in the public health emergency have exposed some limitations of AI in data collecting and unstructured problem-solving. (2) On the supply side, the procedural and substantive rules designed, together with the reliability of an AI system, will shape the performance of the AI service channel. (3) Though AI and other new technologies are advancing drastically in the pandemic, there is still much room for improvement whether by the optimization of AI systems, or by political control and social participation, and by the supplement of alternative channels such as the community service delivery. © 2021 IEEE.

Can the green credit policy stimulate green innovation in heavily polluting enterprises? Evidence from a quasi-natural experiment in China

This paper investigates the effect of the green credit policy (GCP) on green innovation in heavily polluting enterprises (HPEs) using the promulgation of the & ldquo;Green Credit Guidelines & rdquo;(2012 Guidelines) policy in China as a quasi-natural experiment. The findings show that the 2012 Guidelines have had a positive and significant effect on the green patent output of HPEs, especially HPEs under stronger financial constraints. Furthermore, this positive effect occurred predominantly in HPEs with higher expected sunk costs or noncompliance costs, more intense product market competition, and state ownership. These results suggest that the GCP can stimulate green innovation in HPEs by exerting credit constraints, thus achieving green transformation in an emerging economy. (c) 2021 Published by Elsevier B.V.

Heterogeneity learning for SIRS model: an application to the COVID-19

We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest corona virus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains both number of clusters and cluster configurations. Specifically, our key idea is to formulates the SIRS model into a hierarchical form and assign the Mixture of Finite mixtures priors for heterogeneity learning. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze the state level COVID-19 data in U.S.

Data Analytics for the COVID-19 Epidemic

With the spread of COVID-19 worldwide, people¡¯s production and life have been significantly affected. Artificial intelligence and big data technologies have been vigorously developed in recent years. It is very significant to use data science and technology to help humans in a timely and accurate manner to prevent and control the development of the epidemic, maintain social stability and assess the impact of the epidemic. This paper explores how data science can play a role from the perspectives of epidemiology, social networking, and economics. In particular, for the existing epidemic model SIR, we present a parameter learning method using particle swarm optimization (PSO) and the least squares method, and use it to predict the trend of the epidemic. Aiming at the social network data, we provide a specific method to realize sentiment analysis during the epidemic and propose an explainable fake news detection technique based on a variety of data mining methods. © 2020 IEEE.

From guidance to practice: Promoting risk communication and community engagement for prevention and control of coronavirus disease (COVID-19) outbreak in China

It is essential to integrate and function risk communication and community engagement into national public health emergency response. Based on the interim guideline released by WHO and the situation concerning the outbreak of a novel coronavirus in China, this paper suggests that risk communication systems should be enhanced in the highest levels of government. Specifically, internal and partner coordination mechanisms are required to be improved and activated, public communication should be more rapid and accessible, communication engagement with affected communities should be paid more attention, addressing uncertainty and rumor still requires effective measures, and both global collaboration and evidence-based decision-making should be involved in the outbreak control and prevention.