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The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

An outbreak of a novel form of pneumonia due to a coronavirus occurred in Wuhan City, Hubei Province, China, in December 2019, and the disease was officially named coronavirus 2019 (COVID-19). COVID-19 is an infectious disease caused by the SARS-CoV-2 virus which attacks the human respiratory system. In critical cases of COVID-19, special treatment is administered in the ICU using a mechanical ventilator. However, since the results are less than optimal, additional interventions such as orienting the patient in the prone position can be employed, as this can reduce the pressure on the dorsal lung area so that alveolar recruitment can be improved. This study examines the effect of the prone position on increasing pulmonary ventilation. This study is a literature review study employing a narrative method and was conducted by tracing and analyzing articles relevant to the topic. This literature review searched three databases, namely, PubMed, Google Scholar, and ProQuest using the keywords "COVID-19 OR intubated COVID-19" AND "prone position OR mechanical ventilator" AND "Lung ventilation". From these databases, 1062 journals were found and filtered based on inclusion and exclusion criteria, resulting in the inclusion of 10 journals. Then, they were filtered again using a critical appraisal instrument called "JBI Critical Appraisal", which was conducted by two people, N.P. and A.S.; consequently, the results of 5 journals were obtained for further analysis; cumulatively, these papers represented a total population of 1789 patients. It was concluded that the prone position can improve pulmonary ventilation by up to 50% and reduce the total length of stay by up to 5 days in the hospital, as long as the patients do not possess any comorbid diseases. [ABSTRACT FROM AUTHOR]

This paper manages the Origin of Covid (SARS-CoV-2-novel Covid) and the Vaccination view. Within excess of 10 million tainted cases and more than 3 million setbacks at the hour of composing this piece, the original Covid actuated wellbeing disease has arisen as the most serious among every one of the worldwide pandemics. The novel (COVID-19 or 2019-nCoV) was found in Wuhan, Hubei Province, China. Bats were liable for the underlying spread, which was subsequently spread to people by the raccoon canine and palm civet. The normal COVID-19 manifestations are fever, dry hack, and sleepiness. The significant manifestations are trouble breathing, chest torment, and loss of speech. The indications and indications of SARS-CoV-2 prompting COVID-19 completely match those of the occasional or, dust sensitivities and flu as migraine, touchiness of the throat, dry hack, weariness, fever, and at times loss of sensation (Tu et al., year2020). An individual tainted with the diseases of sensitivity to pollen or, flu sensitivity is additionally liable to show temperature that can be recorded with thermo-scanners prompting the individual to turn into a suspect of the sickness. Along these lines, before immunizing, a fast and exact analytic meter or, the pack is the need of great importance to identify the SARS-CoV-2 inferable from the way that the testing in view of PCR is tedious and exorbitant (Sirkeci and Yucesahin, year2020). [ABSTRACT FROM AUTHOR]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

Around the end of 2019, in Wuhan, Hubei Province, China, the first confirmed cases of COVID-19 were identified, and from then on, the world we were used to knowing changed globally. The role of population density, in relation to the spread of the pandemic, has been widely scrutinised in urban studies, believed to be the triggering variable. However, the results so far are inconclusive. This paper suggests instead to shift the focus to socio-spatial vulnerabilities, as the effects of the pandemic's spread have been more severe in urban units which feature long-standing inequalities. The paper's aim is, therefore, twofold: on the one hand it aims at contributing to the debate on population density and COVID-19 in urban areas, and, on the other hand, to analyse the pandemic's spread in relation to socio-spatial vulnerabilities. Different cities across the globe are drawn into a comparative project, where the pandemic's spread is analysed in relation to variables of Population Density (PD) and a Social Vulnerability Index (SVI), by employing correlation matrices. The results suggest that there is no significant correlation between density and the spread of COVID-19. Instead, a positive correlation is in place when analysing the pandemic's diffusion with socio-spatial inequalities. • COVID-19 spread is not associated with population density while social vulnerability is found to be positively correlated. • The study invites to undertake a more critical understanding of how density and vulnerability are intertwined. • Measures based on physical concentration should be supplemented by topological density, made up of relational factors. [ABSTRACT FROM AUTHOR]

In recent years, with the in-depth development of information technology, its role in people's production and life has become increasingly prominent, and it has played a significant role in improving work efficiency. At the beginning of the new year in 2020, the novel coronavirus pneumonia (COVID-19) outbreak broke out in Wuhan City, Hubei Province and spread rapidly across the country. The country attaches great importance to it, and the medical staff acted quickly and actively participated in this war against the virus without the smoke of gunpowder. After arduous efforts from all over the country, our country's COVID-19 epidemic prevention and control situation has been further consolidated, and the prevention and control work has changed from an emergency state to a normal state of prevention and control. Hospital informatization construction is an objective requirement for hospital modernization and the prevention and control of the COVID-19, and it is of great significance in the context of the normalization of the COVID-19 epidemic prevention and control. This paper elaborates on the four aspects of hospital information construction in the background of COVID-19, the existing problems in hospital information construction, the practice of hospital information construction and the exploration of hospital informatization construction, and is committed to exploring the overall thinking of hospital information construction in special period. [ABSTRACT FROM AUTHOR]

This paper treats the outbreak of coronavirus disease 2019 (COVID-19) as a natural experiment that can provide insights into the effects of investor sentiment on stock market reactions. Employing the event study methodology (ESM) and taking the date of the Wuhan lockdown as the event date, we find that average abnormal return (AAR) and cumulative abnormal return (CAR) are significantly negative, and average trading volume excesses far more than before within two days of the outbreak. Further, we establish a difference-in-differences (DID) model to investigate the differences between Hubei and non-Hubei listed companies. The results show that for Hubei listed companies, the change of excessive trading volume (ETV) between pre-event and post-event period is significantly higher than that of non-Hubei listed companies, while there exhibits no relationship between the change of AAR and registration place. Overall, our findings provide new evidence for the interaction of local bias and investor sentiment affecting stock market reactions. [ABSTRACT FROM AUTHOR]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) remains a global pandemic at present. Although the human-to-human transmission route for this disease has been well established, its transmission mechanism is not fully understood. In this paper, we propose a mathematical model for COVID-19 which incorporates multiple transmission pathways and which employs time-dependent transmission rates reflecting the impact of disease prevalence and outbreak control. Applying this model to a retrospective study based on publicly reported data in China, we argue that the environmental reservoirs play an important role in the transmission and spread of the coronavirus. This argument is supported by our data fitting and numerical simulation results for the city of Wuhan, for the provinces of Hubei and Guangdong, and for the entire country of China. [ABSTRACT FROM AUTHOR]

The spatial diffusion of epidemic disease follows distance decay law in geography and social physics, but the mathematical models of distance decay depend on concrete spatio-temporal conditions. This paper is devoted to modeling spatial diffusion patterns of COVID-19 stemming from Wuhan city to Hubei province, China. The modeling approach is to integrate analytical method and experimental method. The local gravity model is derived from allometric scaling and global gravity model, and then the parameters of the local gravity model are estimated by observational data and least squares calculation. The main results are as below. The local gravity model based on power law decay can effectively describe the diffusion patterns and process of COVID-19 in Hubei Province, and the goodness of fit of the gravity model based on negative exponential decay to the observational data is not satisfactory. Further, the goodness of fit of the model to data entirely became better and better over time, the size elasticity coefficient increases first and then decreases, and the distance attenuation exponent decreases first and then increases. Moreover, the significance of spatial autoregressive coefficient in the model is low, and the confidence level is less than 80%. The conclusions can be reached as follows. (1) The spatial diffusion of COVID-19 of Hubei bears long range effect, and the size of a city and the distance of the city to Wuhan affect the total number of confirmed cases. (2) Wuhan direct transmission is the main process in the spatial diffusion of COVID-19 in Hubei at the early stage, and the horizontal transmission between regions is not significant. (3) The effect of spatial lockdown and isolation measures taken by Chinese government against the transmission of COVID-19 is obvious. This study suggests that the role of urban gravity (size and distance) should be taken into account to prevent and control epidemic disease. [ABSTRACT FROM AUTHOR]

At the beginning of 2020, the COVID-19 pandemic was able to spread quickly in Wuhan and in the province of Hubei due to a lack of experience with this novel virus. Additionally, authories had no proven experience with applying insufficient medical, communication and crisis management tools. For a considerable period of time, the actual number of people infected was unknown. There were great uncertainties regarding the dynamics and spread of the Covid-19 virus infection. In this paper, we develop a system dynamics model for the three connected regions (Wuhan, Hubei excl. Wuhan, China excl. Hubei) to understand the infection and spread dynamics of the virus and provide a more accurate estimate of the number of infected people in Wuhan and discuss the necessity and effectivity of protective measures against this epidemic, such as the quarantines imposed throughout China. We use the statistics of confirmed cases of China excl. Hubei. Also the daily data on travel activity within China was utilized, in order to determine the actual numerical development of the infected people in Wuhan City and Hubei Province. We used a multivariate Monte Carlo optimization to parameterize the model to match the official statistics. In particular, we used the model to calculate the infections, which had already broken out, but were not diagnosed for various reasons. [ABSTRACT FROM AUTHOR]

When 2020 began, we had no idea what was to unfold globally as we learnt about the Novel-Coronavirus in Wuhan, in the Hubei province of China. As this virus spread rapidly, it became a matter of time before many countries began to implement measures to try and contain the spread of the disease. COVID-19 as it is referred to, resulted in two main approaches to fighting the viral pandemic, either through a progressive set of measures to slow down the number of identified cases designed to 'flatten the curve' over time (anticipated to be at least six months), or to attack it by the severest of measures including a total lock-down and/or herding exposure to fast track 'immunisation' while we await a vaccine. The paper reports the findings from the first phase of an ongoing survey designed to identify the changing patterns in travel activity of Australian residents as a result of the stage 2 restrictions imposed by the Australian government. The main restrictions, in addition to social distancing of at least 1.5 m, are closure of entry to Australia (except residents returning), and closure of non-essential venues such as night clubs, restaurants, mass attendee sporting events, churches, weddings, and all social gatherings in any circumstance. With some employers encouraging working from home and others requiring it, in addition to job losses, and many children attending school online from home, the implications on travel activity is extreme. We identify the initial impacts associated with the first month of stricter social distancing measures introduced in Australia. • General support for the actions of government and business. • Widespread suppression of travel demand for all trip purposes across all modes. • Sizeable shift to working from home by those who can. • Public transport will face the largest hurdles in regaining confidence. • Retail and food logistics will need to examine distribution processes • The aviation sector will likely need to restart with a heavy focus on domestic travel. • Public transport will need to take overt measures to restore confidence. • The dominance of the car is further enforced in the context of biosecurity concerns. • Active transport is a viable option for short inner-city trips. • Infrastructure investment should be carefully considered. • Flexible working arrangements are perhaps the biggest policy lever available to governments. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic started in Wuhan in December 2019 and then spread to the rest of China and the world. This paper examines the relationship between inter-provincial migration and the diffusion of covid-19 outbreak in Mainland China from 26 January to 6 March 2020. Statistical analysis shows that the migrants who moved into Hubei in 2010–2015 are a good indicator of the population flow from Hubei to other provinces that caused the spread of covid-19 in early 2020. Many migrants traveled back to hometown before the traffic ban of Wuhan city for the spring holiday. Thus, the migration flow from a province to Hubei in the period 2010–2015 had the highest correlation coefficient with the number of covid-19 cases in four selected dates. The population flow based on the big data from the Baidu map on 20 January 2020 was also highly correlated with the number of cases, but not as good as the above migration data. The finding demonstrates the role of established migrant networks in the spread of covid-19. [ABSTRACT FROM AUTHOR]

In December 2019, the first cases of a new contagious disease were diagnosed in the city of Wuhan, the capital of Hubei province in China. Within a short period of time the outbreak developed exponentially into a pandemic that infected millions of people, with a global death toll of more than 500,000 during its first 6 months. Eventually, the novel disease was named coronavirus disease 2019 (COVID-19), and the new virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Similar to all known pandemics throughout history, COVID-19 has been accompanied by a large degree of fear, anxiety, uncertainty, and economic disaster worldwide. Despite multiple publications and increasing knowledge regarding the biological secrets of SARS-CoV-2, as of the writing of this paper, there is neither an approved vaccine nor medication to prevent infection or cure for this highly infectious disease. Past pandemics were caused by a wide range of microbes, primarily viruses, but also bacteria. Characteristically, a significant proportion of them originated in different animal species (zoonoses). Since an understanding of the microbial cause of these diseases was unveiled relatively late in human history, past pandemics were often attributed to strange causes including punishment from God, demonic activity, or volatile unspecified substances. Although a high case fatality ratio was common to all pandemic diseases, some striking clinical characteristics of each disease allowed contemporaneous people to clinically diagnose the infection despite null microbiological information. In comparison to past pandemics, SARS-CoV-2 has tricky and complex mechanisms that have facilitated its rapid and catastrophic spread worldwide. [ABSTRACT FROM AUTHOR]

We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and 95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases. [ABSTRACT FROM AUTHOR]

An outbreak of the so-called Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), has been spreading rapidly nationwide in China since December 2019. Wuhan, Hubei Province, is the hardest-hit region, with a rise in confirmed cases and its hospitals overwhelmed. On 2nd February, 2020, Wuhan began to build a modular hospital to treat patients caught with mild illness. The mobile modular hospital is mainly composed of medical modules, technical support modules, ward units, living support units and transportation capacity under field conditions, and there are complete equipment and specialized personnel to treat patients. Due to the severity and particularity of SARS-CoV-2, taking granted from lessons learnt from mobile modular hospitals, we use the existing large venues to construct a new fixed modular hospital. As patients need to be treated and tested, it is important to develop a clinical laboratory in the modular hospital and ensure biosafety. The construction of a clinical laboratory in the modular hospital is faced with problems such as time pressure, limited site selection, high level of biosafety, lack of experience and so forth. This paper mainly discusses how to construct the clinical laboratory in the modular hospital quickly and safely and put it into use to provide testing service for patients under various limited conditions. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non- healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals. [ABSTRACT FROM AUTHOR]

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables. • COVID-19 had a big impact in Italy, mainly in the northern part of the Country. • Reduction of land take and ecosystem services' loss improve environmental quality. • Environmental, climatic and land management can be leveraged for pandemic policies. • Geospatial representation of risks is a useful approach to support decision-makers. [ABSTRACT FROM AUTHOR]

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research. • A novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions. • An empirical analysis was carried out based proposed framework using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). • An open-source dataset of HCW diagnoses is provided, which both ensures the reproducibility of the study and provides the data needed to support related research on data under-reporting. [ABSTRACT FROM AUTHOR]

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts. [ABSTRACT FROM AUTHOR]

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei's PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers. [ABSTRACT FROM AUTHOR]

Clarifying the regional transmission mechanism of COVID-19 has practical significance for effective protection. Taking 103 county-level regions of Hubei Province as an example, and taking the fastest-spreading stage of COVID-19, which lasted from 29 January 2020, to 29 February 2020, as the research period, we systematically analyzed the population migration, spatio-temporal variation pattern of COVID-19, with emphasis on the spatio-temporal differences and scale effects of related factors by using the daily sliding, time-ordered data analysis method, combined with extended geographically weighted regression (GWR). The results state that: Population migration plays a two-way role in COVID-19 variation. The emigrants' and immigrants' population of Wuhan city accounted for 3.70% and 73.05% of the total migrants' population respectively; the restriction measures were not only effective in controlling the emigrants, but also effective in preventing immigrants. COVID-19 has significant spatial autocorrelation, and spatio-temporal differentiation has an effect on COVID-19. Different factors have different degrees of effect on COVID-19, and similar factors show different scale effects. Generally, the pattern of spatial differentiation is a transitional pattern of parallel bands from east to west, and also an epitaxial radiation pattern centered in the Wuhan 1 + 8 urban circle. This paper is helpful to understand the spatio-temporal evolution of COVID-19 in Hubei Province, so as to provide a reference for similar epidemic prevention. [ABSTRACT FROM AUTHOR]