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This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers' fear of infection. [ABSTRACT FROM AUTHOR]

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics. [ABSTRACT FROM AUTHOR]

In mid-December 2019, a novel atypical pneumonia broke out in Wuhan, Hubei Province, China and was caused by a newly identified coronavirus, initially termed 2019 Novel Coronavirus and subsequently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 19 May 2020, a total of 4,731,458 individuals were reported as infected with SARS-CoV-2 among 213 countries, areas or territories with recorded cases, and the overall case-fatality rate was 6.6% (316,169 deaths among 4,731,458 recorded cases), according to the World Health Organization. Studies have shown that SARS-CoV-2 is notably similar to (severe acute respiratory syndrome coronavirus) SARS-CoV that emerged in 2002-2003 and Middle East respiratory syndrome coronavirus (MERS-CoV) that spread during 2012, and these viruses all contributed to global pandemics. The ability of SARS-CoV-2 to rapidly spread a pneumonia-like disease from Hubei Province, China, throughout the world has provoked widespread concern. The main symptoms of coronavirus disease 2019 (COVID-19) include fever, cough, myalgia, fatigue and lower respiratory signs. At present, nucleic acid tests are widely recommended as the optimal method for detecting SARS-CoV-2. However, obstacles remain, including the global shortage of testing kits and the presentation of false negatives. Experts suggest that almost everyone in China is susceptible to SARS-CoV-2 infection, and to date, there are no effective treatments. In light of the references published, this review demonstrates the biological features, spread, diagnosis and treatment of SARS-CoV-2 as a whole and aims to analyse the similarities and differences among SARS-CoV-2, SARS-CoV and MERS-CoV to provide new ideas and suggestions for prevention, diagnosis and clinical treatment. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus diseases 2019 (COVID-19) caused over 1.7 million confirmed cases and cumulative mortality up to over 110,000 deaths worldwide as of 14 April 2020. A total of 57 Macao citizens were obligated to stay in Hubei province, China, where the highest COVID-19 prevalence was noted in the country and a "lockdown" policy was implemented for outbreak control for more than one month. They were escorted from Wuhan City to Macao via a chartered airplane organized by Macao SAR government and received quarantine for 14 days with none of the individual being diagnosed with COVID-19 by serial RNA tests from the nasopharyngeal specimens and sera antibodies. It was crucial to identify common characteristics among these 57 uninfected individuals. Methods: A questionnaire survey was conducted to extract information such as behavior, change of habits and preventive measures. Results: A total of 42 effective questionnaires were analyzed after exclusion of 14 infants and children with age under fifteen as ineligible for the survey and missing of one questionnaire, with a response rate of 97.7% (42 out of 43). The proportion of female composed more than 70% of this group of returners. The main reason for visiting Hubei in 88.1% of respondents was to visit relatives. Over 88% of respondents did not participate in high-risk activities due to mobility restriction. All (100%) denied contact with suspected or confirmed COVID-19 cases. Comparison of personal hygiene habits before and during disease outbreak showed a significant increase in practice including wearing a mask when outdoor (16.7% and 95.2%, P < 0.001) and often wash hands with soap or liquid soap (85.7% and 100%, P = 0.031). [ABSTRACT FROM AUTHOR]

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses' dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter's performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation. [ABSTRACT FROM AUTHOR]

A sequence of pneumonia-causing diseases from the Novel coronavirus (COVID-19) appeared in Wuhan, Hubei, China in December 2019. The outbreak of COVID-19 spread quite rapidly. Just as we write this report, almost two and a half million verified cases were reported globally, and almost 180,000 people died.1 Experience from China reveals that COVID-19 outbreaks can be brought under control within 3 months, with highly efficient touch tracking and case isolation.2 Healthcare staff are at the forefront of treatment for COVID-19 cases and have a very large chance of exposure to the infection.3 Cuts in disposable gear and COVID-19 awareness are triggering infections in healthcare workers. 4 As of February 11, 2020, China has contaminated more than 1,700 health-care workers. In Italy, 2026 (9 percent) of the COVID-19 incidents happened in healthcare professionals (as of March 15). 5 But, 31 medical teams containing more than 42,000 nurses and doctors sent by the Hubei from other provinces did not have a reported infection. It indicates that sufficient information about the transmission of disease and the use of protective equipment and procedures to manage infections is necessary to avoid the spread of infection among health care staff. [ABSTRACT FROM AUTHOR]

It is essential to understand where and how severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is transmitted. Case reports were extracted from the local Municipal Health Commissions of 320 prefectural municipalities in China (not including Hubei Province). We identified all outbreaks involving three or more cases and reviewed the major characteristics of the enclosed spaces in which the outbreaks were reported and their associated indoor environmental aspects. Three hundred and eighteen outbreaks with three or more cases were identified, comprising a total of 1245 confirmed cases in 120 prefectural cities. Among the identified outbreaks, 53.8% involved three cases, 26.4% involved four cases, and only 1.6% involved ten or more cases. Home‐based outbreaks were the dominant category (254 of 318 outbreaks; 79.9%), followed by transport‐based outbreaks (108; 34.0%), and many outbreaks occurred in more than one category of venue. All identified outbreaks of three or more cases occurred in indoor environments, which confirm that sharing indoor spaces with one or more infected persons is a major SARS‐CoV‐2 infection risk. [ABSTRACT FROM AUTHOR]

• A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate. • Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO 2 emission and population density) are used in the model. • The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China. • A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase. • The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results. COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO 2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results. [ABSTRACT FROM AUTHOR]

A novel coronavirus (SARS-CoV-2), causing an emerging coronavirus disease (COVID-19), first detected in Wuhan City, Hubei Province, China, which has taken a catastrophic turn with high toll rates in China and subsequently spreading across the globe. The rapid spread of this virus to more than 210 countries while affecting more than 25 million people and causing more than 843,000 human deaths, it has resulted in a pandemic situation in the world. The SARS-CoV-2 virus belongs to the genus Betacoronavirus, like MERS-CoV and SARS-CoV, all of which originated in bats. It is highly contagious, causing symptoms like fever, dyspnea, asthenia and pneumonia, thrombocytopenia, and the severely infected patients succumb to the disease. Coronaviruses (CoVs) among all known RNA viruses have the largest genomes ranging from 26 to 32 kb in length. Extensive research has been conducted to understand the molecular basis of the SARS-CoV-2 infection and evolution, develop effective therapeutics, antiviral drugs, and vaccines, and to design rapid and confirmatory viral diagnostics as well as adopt appropriate prevention and control strategies. To date, August 30, 2020, no effective, proven therapeutic antibodies or specific drugs, and vaccines have turned up. In this review article, we describe the underlying molecular organization and phylogenetic analysis of the coronaviruses, including the SARS-CoV-2, and recent advances in diagnosis and vaccine development in brief and focusing mainly on developing potential therapeutic options that can be explored to manage this pandemic virus infection, which would help in valid countering of COVID-19. [ABSTRACT FROM AUTHOR]

In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been ≤ 1.0% since Mid-February. The LSTM model showed comparable accuracy. The GWR model took into account the influence of geographical differences, with R2 = 99.98% in fitting and 97.95% in prediction. Wilcoxon test showed that none of the three models outperformed the other two at the significance level of 0.05. The parametric analysis of the infectious rate and recovery rate demonstrated that China's national policies had effectively slowed down the spread of the epidemic. Furthermore, the models in this study provided a wide range of implications for other countries to predict the short-term and long-term trend of COVID-19, and to evaluate the intensity and effect of their interventions. [ABSTRACT FROM AUTHOR]

The world entered the year 2020 with reports of the emergence of a new viral illness in Wuhan city, Hubei province, China. In January 2020, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was identified to be the causative novel coronavirus for the cluster of patients suffering from pneumonia in China. The disease was later named as coronavirus disease (COVID‐19) and was declared a pandemic by the World Health Organization on March 11, 2020. Several studies, since then, have tried to study and explain the origin of SARS‐CoV‐2, its structure and pathogenicity, epidemiology, modes of transmission, spectrum of illness and causes of mortality and morbidity. The current management strategies focus on supportive care and prevention of complications. With no definite treatment, as of now, encouraging reports of some anti‐viral and anti‐malarial drugs in the management of COVID‐19 generate some hope. This review intends to cover the current known aspects of COVID‐19 and SARS‐CoV‐19, based on the available literature. [ABSTRACT FROM AUTHOR]

Background: On December 31, 2019, the World Health Organization was alerted to the occurrence of cases of pneumonia in Wuhan, Hubei Province, China, that were caused by an unknown virus, which was later identified as a coronavirus and named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate the reproductive number of SARS-CoV-2 in the Hubei Province and evaluate the risk of an acute respiratory coronavirus disease (COVID-19) outbreak outside China by using a mathematical model and stochastic simulations. Results: We constructed a mathematical model of SARS-CoV-2 transmission dynamics, estimated the rate of transmission, and calculated the reproductive number in Hubei Province by using case-report data from January 11 to February 6, 2020. The possible number of secondary cases outside China was estimated by stochastic simulations in various scenarios of reductions in the duration to quarantine and rate of transmission. The rate of transmission was estimated as 0.8238 (95% confidence interval [CI] 0.8095–0.8382), and the basic reproductive number as 4.1192 (95% CI 4.0473–4.1912). Assuming the same rate of transmission as in Hubei Province, the possibility of no local transmission is 54.9% with a 24-h quarantine strategy, and the possibility of more than 20 local transmission cases is 7% outside of China. Conclusion: The reproductive number for SARS-CoV-2 transmission dynamics is significantly higher compared to that of the previous SARS epidemic in China. This implies that human-to-human transmission is a significant factor for contagion in Hubei Province. Results of the stochastic simulation emphasize the role of quarantine implementation, which is critical to prevent and control the SARS-CoV-2 outbreak outside China. [ABSTRACT FROM AUTHOR]

The emergence of a novel Coronavirus disease (COVID-19) inducing acute respiratory distress syndrome (ARDS) was identified in Hubei province of China in December 2019 and rapidly spread worldwide as pandemic and became a public health concern. COVID-19 disease is caused by a new virus known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), which has recently offered many challenges and efforts to identify effective drugs for its prevention and treatment. Currently, there is no proven effective approach and medication against this virus. Quickly expanding clinical trials and studies on Coronavirus disease 2019 increase our knowledge regarding SARS-CoV-2 virus and introduce several potential drugs targeting virus moiety or host cell elements. Overall, 3 stages were suggested for SARS-CoV-2 infection according to the disease severity, clinical manifestations, and treatment outcomes, including mild, moderate, and severe. This review aimed to classify and summarize several medications and potential therapies according to the disease 3 stages; however, it is worth noting that no medication and therapy has been effective so far. [ABSTRACT FROM AUTHOR]

Background: The main objective of this study was to use citation networks to analyze the relationship between different publications on the impact of COVID-19 at an ocular level and their authors. Furthermore, the different research areas will be identified, and the most cited publication will be determined. Materials and Methods: The publications were searched within the Web of Science database, using "ocular", "SARS-CoV-2", "ophthalmology", "eyesight", and "COVID-19" as keywords for the period between January 2020 and January 2021. The Citation Network Explorer and the CiteSpace software were used to analyze the different publications. Results: A total of 389 publications with 890 citations generated on the web were found. It must be highlighted that July was the month with the largest number of publications. The most cited ones were "Characteristics of Ocular Findings of Patients with Coronavirus Disease 2019 (COVID-19) in Hubei Province, China" by Wu et al., which was published in May 2020. Three groups covering the different research areas in this field were found using the clustering functions: ocular manifestations, teleophthalmology, and personal protective equipment. Conclusions: The citation network has shown a comprehensive and objective analysis of the main studies on the impact of COVID-19 in ocular disease. [ABSTRACT FROM AUTHOR]

Three major outbreaks of the coronavirus, a zoonotic virus known to cause respiratory disease, have been reported since 2002, including SARS-CoV, MERS-CoV and the most recent 2019-nCoV, or more recently known as SARS-CoV-2. Bats are known to be the primary animal reservoir for coronaviruses. However, in the past few decades, the virus has been able to mutate and adapt to infect humans, resulting in an animal-to-human species barrier jump. The emergence of a novel coronavirus poses a serious global public health threat and possibly carries the potential of causing a major pandemic outbreak in the naïve human population. The recent outbreak of COVID-19, the disease caused by SARS-CoV-2, in Wuhan, Hubei Province, China has infected over 36.5 million individuals and claimed over one million lives worldwide, as of 8 October 2020. The novel virus is rapidly spreading across China and has been transmitted to 213 other countries/territories across the globe. Researchers have reported that the virus is constantly evolving and spreading through asymptomatic carriers, further suggesting a high global health threat. To this end, current up-to-date information on the coronavirus evolution and SARS-CoV-2 modes of transmission, detection techniques and current control and prevention strategies are summarized in this review. [ABSTRACT FROM AUTHOR]