Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)epidemic.On governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,and they feel challenging to tackle this situation.Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations.In the previous works,Long Short-Term Memory(LSTM)was used to predict future COVID-19 cases.According to LSTM network data,the outbreak is expected tofinish by June 2020.However,there is a chance of an over-fitting problem in LSTM and true positive;it may not produce the required results.The COVID-19 dataset has lower accuracy and a higher error rate in the existing system.The proposed method has been introduced to overcome the above-mentioned issues.For COVID-19 prediction,a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network(LDIWCSO-HBDCNN)approach is presented.In this suggested research study,the COVID-19 predicting dataset is employed as an input,and the min-max normalization approach is employed to normalize it.Optimum features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization(LDIWCSO)algorithm,enhancing the accuracy of classification.The Cat Swarm Optimization(CSO)algorithm’s convergence is enhanced using inertia weight in the LDIWCSO algorithm.It is used to select the essential features using the bestfitness function values.For a specified time across India,death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network(HBDCNN)technique based on selected features.As demonstrated by empirical observations,the proposed system produces significant performance in terms of f-measure,recall,precision,and accuracy.

Impact of lockdown policies during the COVID-19 outbreak on a trauma center of a tertiary hospital in China

BACKGROUND Coronavirus disease 2019(COVID-19)is a major and costly public health emergency.AIM To investigate the impact of China’s lockdown policies during the COVID-19 outbreak on the level I trauma center of a tertiary comprehensive hospital of Traditional Chinese Medicine.METHODS All patients admitted to our trauma center during a lockdown in 2020 and the same period in 2019 were enrolled.We collected data on demographics,daily visits,injury type,injury mechanism,injury severity score,and patient management for comparative analysis.RESULTS The total number of patients in the trauma center of our hospital decreased by 50.38%during the COVID-19 Lockdown in 2020 compared to the same period in 2019.The average number of trauma visits per day in 2019 was 47.94,compared to 23.79 in 2020.Comparing the patients’demographic data,loss of employment was the most predominate characteristic in 2020 compared to 2019,while there was no significant difference in gender,age,and marital status between both periods.During the lockdown period,the proportion of traffic accident-related injuries,injuries due to falls greater than 1.5 m,and mechanical injuries decreased significantly,whereas the proportion of injuries caused by falls less than 1.5 m,cuts,assault,bites,and suicidal tendencies and other injuries increased relatively.In addition,the proportion of patients with minor injuries increased and serious injuries decreased during the lockdown.The hospitalization rate increased significantly,and there was no significant difference in emergency surgery and death rates.CONCLUSION The lockdown policies during the COVID-19 outbreak significantly altered the number and mechanism of traumatic events in our hospital,which can be monitored regularly.Our results suggest that mandatory public health prevention and control measures by the government can reduce the incidence of traumatic events and the severity of traumatic injuries.Emergency surgery and mortality rates remain high,increased because of factors such as family injury and penetrating injury,and hospitalization rates have increased significantly.Therefore,our trauma center still needs to be fully staffed.Finally,from the perspective of the injury mechanism,indoor trauma is a major risk during a lockdown,and it is particularly important to develop prevention strategies for such trauma to reduce the medical burden of the next catastrophic epidemic.

Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

Recent studies have pointed out the potential of the odd Fréchet family(or class)of continuous distributions in fitting data of all kinds.In this article,we propose an extension of this family through the so-called“Topp-Leone strategy”,aiming to improve its overall flexibility by adding a shape parameter.The main objective is to offer original distributions with modifiable properties,from which adaptive and pliant statistical models can be derived.For the new family,these aspects are illustrated by the means of comprehensive mathematical and numerical results.In particular,we emphasize a special distribution with three parameters based on the exponential distribution.The related model is shown to be skillful to the fitting of various lifetime data,more or less heterogeneous.Among all the possible applications,we consider two data sets of current interest,linked to the COVID-19 pandemic.They concern daily cases confirmed and recovered in Pakistan from March 24 to April 28,2020.As a result of our analyzes,the proposed model has the best fitting results in comparison to serious challengers,including the former odd Fréchet model.

Dynamical Analysis of the Stochastic COVID-19 Model Using Piecewise Differential Equation Technique

Various data sets showing the prevalence of numerous viral diseases have demonstrated that the transmission is not truly homogeneous.Two examples are the spread of Spanish flu and COVID-19.The aimof this research is to develop a comprehensive nonlinear stochastic model having six cohorts relying on ordinary differential equations via piecewise fractional differential operators.Firstly,the strength number of the deterministic case is carried out.Then,for the stochastic model,we show that there is a critical number RS0 that can predict virus persistence and infection eradication.Because of the peculiarity of this notion,an interesting way to ensure the existence and uniqueness of the global positive solution characterized by the stochastic COVID-19 model is established by creating a sequence of appropriate Lyapunov candidates.Adetailed ergodic stationary distribution for the stochastic COVID-19 model is provided.Our findings demonstrate a piecewise numerical technique to generate simulation studies for these frameworks.The collected outcomes leave no doubt that this conception is a revolutionary doorway that will assist mankind in good perspective nature.

The aggregate and sectoral time‑varying market efficiency during crisis periods in Turkey:a comparative analysis with COVID‑19 outbreak and the global financial crisis

This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.

Minimizing the risk of international spread of coronavirus disease 2019 (COVID-19) outbreak by targeting travelers

The Coronavirus Disease-2019 (COVID-19) outbreak has become a global health emergency owing to its magnitude, attributed deaths, and its propensity to spread across the world. In-fact, owing to its quick spread across international boundaries and the resulting caseload, the disease has been declared as a Public Health Emergency of International Concern on 30 January 2020. It is worth noting that out of the 395 cases detected in other nations, 165 (41.8%) have a positive history of travel to China. As of now, the World Health Organization has not recommended for any restrictions on the travel or trade aspects, but has clearly specified that implementation of International Health Regulations should be strictly done at the airports and seaports. In conclusion, the COVID-19 outbreak has created an alarm across the globe as the causative virus is novel in nature. However, strengthening of standard infection control practices and adoption of preventive measures for travelers can significantly minimize the threat of further transmission of the disease.

Combined cesarean delivery and repair of acute aortic dissection at 34 weeks of pregnancy during COVID-19 outbreak:A case report

BACKGROUND Since the outbreak of the coronavirus disease 2019(COVID-19)pandemic,the exclusion of a patient from COVID-19 should be performed before surgery.However,patients with type A acute aortic dissection(AAD)during pregnancy can seriously endanger the health of either the mother or fetus that requires emergency surgical treatment without the test for COVID-19.CASE SUMMARY A 38-year-old woman without Marfan syndrome was admitted to the hospital because of chest pain in the 34th week of gestation.She has diagnosed as having a Stanford type-A AAD involving an aortic arch and descending aorta via aortic computed tomographic angiography.The patient was transferred to the isolated negative pressure operating room in one hour and underwent cesarean delivery and ascending aorta replacement.All medical staff adopted third-level medical protection measures throughout the patient transfer and surgical procedure.After surgery,the patient was transferred to the isolated negative pressure intensive care unit ward.The nucleic acid test and anti-COVID-19 immunoglobulin(Ig)G and IgM were performed and were negative.The patient and infant were discharged without complication nine days later and recovered uneventfully.CONCLUSION The results indicated that the procedure that we used is feasible in patients with a combined cesarean delivery and surgery for Stanford type-A AAD during the COVID-19 outbreak,which was mainly attributed to rapid multidisciplinary consultation,collaboration,and quick decision-making.

How Is the COVID-19 Outbreak Affecting Wildlife around the World?

The COVID-19 is the infectious disease caused by the most recently discovered coronavirus at an animal market in Wuhan, China. Many wildlife species have been suggested as possible intermediate sources for the transmission of COVID-19 virus from bats to humans. The quick transmission of COVID-19 outbreak has imposed quarantine measures across the world, and as a result, most of the world’s towns and cities fell silent under lockdowns. The current study comes to investigate the ways by which the COVID-19 outbreak affects wildlife globally. Hundreds of internet sites and scientific reports have been reviewed to satisfy the needs of the study. Stories of seeing wild animals roaming the quiet, deserted streets and cities during the COVID-19 outbreak have been posted in the media and social media. The strong link between wildlife markets and COVID-19 resulted in international calls asking countries to shut down wildlife markets forever. Poorer and vulnerable people around the world overexploit natural resources including wildlife. Roadkills became minimal because of the lockdown measures. The reduction in noise pollution level is expected to improve wildlife health and ecology including breeding success. The shortage of food items provided to zoo and park animals constituted a real threat to animals and the institution harboring them. The increase in fish biomass comes as a result of the sharp decline in fishing activities. The isolation of antibodies from certain wildlife species is promising in saving humankind against COVID-19. The infection of wild and pet animals with COVID-19 virus from humans and the interspecific transmission of the infection are disastrous to animal ecology. Finally, closures may enhance people to connect more and more with nature in order to acknowledge wildlife in their surrounding environments. In conclusion, the study asks the world’s different parties to conserve wildlife in a sustainable fashion and to regulate exotic animal trade in wet markets in order to lower the incidence of zoonoses.

Tradeoffanalysis of the pork supply and food security under the influence of African swine fever and the COVID-19 outbreak in China

China is the world’s largest consumer of pork and grains.However,African swine fever(ASF)and the COVID-19 outbreak have greatly impacted the pork supply and food security in China.How can food security and the pork supply be ensured under the dual impacts of COVID-19 and ASF?This is a major problem to be urgently solved by the Chinese government.This study indicated that the main pork production and sales areas in China were separated,which reflected the spatial imbalance between the supply and demand.The total area of suitable selected sites for pig farms in China is 21.5 million ha.If only the areas with levels of high and moderate suitability are considered as potential sites for pig farms,the potential pork production can reach 56.1 million tons in China,which is slightly lower than demand.Due to the impact of the ASF epidemic,the food consumed by pigs has been reduced by 34.7 million tons.However,with increasing pork productivity in the future,the self-sufficiency rate of grains may further decline.On the premise that the quality of people’s life is not affected,the diversification of meat supply channels should be realized in an orderly and sustainable way,which might alleviate the pressure on food supply.This study provides a theoretical reference for the spatiotemporal layout of the swine industry and addresses the issue of food security in China under the influence of ASF and the COVID-19 outbreak.

Study on soybean potential productivity and food security in China under the influence of COVID-19 outbreak

The COVID-19 outbreak that became a global pandemic in early 2020 is starting to affect agricultural supply chains and leading to a rapid rise in global food prices.As many grain exporting countries announced a ban on grain exports,food security issues in China have attracted a significant international attention.Based on the Suitability Distribution Model and Soybean-Cereal Constraint Model,we explored the relationship between soybean production potential and food security.We calculated that the soybean potential planting area in China is 164.3 million ha.If the outbreak prevents China from importing soybeans,soybean planting area will need to be increased by 6.9 times to satisfy the demands.In the meantime,cereal self-sufficiency rate will drop to 63.4%,which will greatly affect food security.Each additional unit of soybean production will reduce 3.9 units of cereal production,and 1%increase in the self-sufficiency rate of soybean will result in a 0.63%drop in the self-sufficiency rate of cereal.Without sacrificing the self-sufficiency rate of cereal,the self-sufficiency rate of soybean is limited to 42%.Consequently,China will still need to import more than 68%of the current import volume of soybean.Although in the short term,the outbreak will not affect food security in China,as soybean imports decrease,insufficient supply of soybeans will affect people’s quality of life.To prevent the impact of the COVID-19 outbreak,China should increase soybean stocks and strengthen international cooperation.In the long term,increasing the self-sufficiency rate is a fundamental solution to solving soybean import dependency.The key to increasing soybean cultivation is by making soybean cultivation profitable and by building a sustainable soybean planting chain.

Predictive modelling for COVID-19 outbreak control:lessons from the navy cluster in Sri Lanka

In response to an outbreak of coronavirus disease 2019(COVID-19)within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020,an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health.To predict the possible number of cases within the susceptible population under four social distancing scenarios,the COVID-19 Hospital Impact Model for Epidemics(CHIME)was used.With increasing social distancing,the epidemiological curve flattened,and its peak shifted to the right.The observed or actually reported number of cases was above the projected number of cases at the onset;however,subsequently,it fell below all predicted trends.Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.

Emergency surgery in COVID-19 outbreak:Has anything changed?Single center experience

BACKGROUND The current coronavirus disease 19(COVID-19)pandemic is changing the organization of health care and has had a direct impact on the management of surgical patients.At the General Surgery Department of Sant’Anna University Hospital in Ferrara,Italy,surgical activities were progressively reduced during the peak of the COVID-19 outbreak in Italy.During this period,only one operating room was available for elective cancer surgeries and another for emergency surgeries.Moreover,the number of beds for surgical patients had to be reduced to provide beds and personnel for the new COVID-19 wards.AIM To compare 2 different period(from March 9 to April 92019 and from March 9 to April 92020),searching differences in terms of number and type of interventions in emergency surgery of a main University Hospital in Ferrara,a city in Emilia Romagna region,North of Italy.METHODS This retrospective study was carried out at the General Surgery Department of Sant’Anna University Hospital in Ferrara,Italy.We examined the number of emergency surgeries performed and patient outcomes during the peak of the COVID-19 outbreak in Italy and subsequent total lockdown.We then drew a comparison with the number of surgeries performed and their outcomes during the same period in 2019.The study examined all adult patients who underwent emergency surgery from March 9 to April 9,2019(n=46),and those who underwent surgery during the first month of the lockdown,from March 9 to April 9,2020(n=27).Analyses were adjusted for age,gender,American Society of Anesthesiologists classification scores and types of surgery.RESULTS A total of 27 patients underwent emergency surgery at Sant’Anna University Hospital in Ferrara during the first month of the lockdown.This represents a 41.3%reduction in the number of patients who were hospitalized and underwent emergency surgery compared to the same period in 2019.The complication rate during the pandemic period was substantially higher than it was during the analogous period in 2019:15 out of 27 cases from March 9 to April 9,2020(55)vs 17 out of 46 cases from March 9 to April 9,2019(36.9).Of the 27 patients who underwent emergency surgery during the pandemic,10 were screened for COVID-19 using both thorax high resolution computerized tomography and a naso-pharyngeal swab,while 9 only underwent thorax high resolution computerized tomography.Only 1 patient tested positive for SARS-CoV-2 and died following surgery.CONCLUSION There was a significant reduction in emergency surgeries at our center during the COVID-19 pandemic,and it is plausible that there were analogous reductions at other centers across Italy.

Simple Simulation for COVID-19 in Wuhan, Hubei, China to Predict Outbreak in Wuhan, the Initial Case in Wuhan, and the Epidemic in Japan as of 11 February, 2020

Object: Prediction of the COVID-19 epidemic represents a matter of concern not only for public health or medicine but also for Earth’s general population. This study predicts outbreaks in Wuhan and in Japan as of 11 February, 2020.Method: We applied a simple SIR model to data published by Hubei public health authorities. Moreover, into the model, we incorporate mild and asymptomatic cases from experiences of Japanese residents of Wuhan up to the outbreak. Finally, we predict an outbreak in Japan based on 10,000 iterations of a simulation conducted under the assumption of infected people including mild cases visiting Japan according to the estimated distribution of patients in Wuhan since the date on which the initial case occurred to the date when travel from Wuhan to Japan was suspended. Results: Results suggest the basic reproduction number, R0, as 2.84;its 95% confidence interval (CI) was [2.35, 3.33]. The peak is estimated to be reached on March 11. Its 95% CI peak date is 29 February to 27 March. The 95% CI peak date in Japan is 26 April to 2 May. The greatest number of patients at the peak with severe symptoms was estimated as 858.3 thousand. Discussion and Conclusion: Our obtained R0 of 2.84 approximates an earlier estimate. We predicted the greatest number of patients at the peak with severe symptoms as 858.3 thousand in Japan. This number is 63% greater than the highest daily peak of influenza.

No increase in burnout in health care workers during the initial COVID-19 outbreak:Systematic review and meta-analysis

BACKGROUND For decades and before the coronavirus disease 2019(COVID-19)pandemic,for health care workers(HCWs)burnout can be experienced as an upsetting confrontation with their self and the result of a complex a multifactorial process interacting with environmental and personal features.AIM To literature review and meta-analysis was to obtain a comprehensive understanding of burnout and work-related stress in health care workers around the world during the first outbreak of the COVID-19 pandemic.METHODS We performed a database search of Embase,Google Scholar and PubMed from June to October 2020.We analysed burnout risk factors and protective factors in included studies published in peer-reviewed journals as of January 2020,studying a HCW population during the first COVID-19 wave without any geographic restrictions.Furthermore,we performed a meta-analysis to determine overall burnout levels.We studied the main risk factors and protective factors related to burnout and stress at the individual,institutional and regional levels.RESULTS Forty-one studies were included in our final review sample.Most were crosssectional,observational studies with data collection windows during the first wave of the COVID-19 surge.Of those forty-one,twelve studies were included in the meta-analysis.Of the 27907 health care professionals who participated in the reviewed studies,70.4%were women,and two-thirds were either married or living together.The most represented age category was 31-45 years,at 41.5%.Approximately half of the sample comprised nurses(47.6%),and 44.4%were working in COVID-19 wards(intensive care unit,emergency room and dedicated internal medicine wards).Indeed,exposure to the virus was not a leading factor for burnout.Our meta-analytic estimate of burnout prevalence in the HCW population for a sample of 6784 individuals was 30.05%.CONCLUSION There was a significant prevalence of burnout in HCWs during the COVID-19 pandemic,and some of the associated risk factors could be targeted for intervention,both at the individual and organizational levels.Nevertheless,COVID-19 exposure was not a leading factor for burnout,as burnout levels were not notably higher than pre-COVID-19 levels.

云南省瑞丽市景观生态风险及其与COVID-19疫情分布的耦合关系

[目的]探究生态风险时空演变和其与COVID-19疫情空间分布关系,为区域的生态环境保护、可持续发展和疫情管控提供理论参考。[方法]以云南省瑞丽市为研究区,基于Landsat遥感影像,提取2000,2010,2015和2021年4期景观类型数据,运用景观人工干扰强度、景观生态风险评价模型和GWR模型等方法,定量研究2000—2021年瑞丽市景观类型和生态风险时空变化,进而研究景观人工干扰强度和生态风险分别与COVID-19疫情空间分布的关系。[结果](1)2000—2021年瑞丽市景观类型主要以林地和园地为主;林园地面积相对稳定,耕地面积出现波动,建设用地不断扩张;(2)2000—2021年瑞丽市景观人工干扰强度以低强度为主,以点状向外缓慢增加,整体趋势向西北—东南方向扩张,中高强度区域面积不断增大,南部沿瑞丽江区域强度逐渐加深;(3)瑞丽市景观生态风险等级以低和较低风险为主,并呈现由低等级向高等级转移趋势;低风险区主要分布在中部;中等级及以上风险区主要分布在西北和东南部。各时期景观生态风险在空间上以高—高和低—低聚集,有较强的自相关性;(4)2021年瑞丽市COVID-19疫情空间分布呈现“一主两次”中心的聚集特征,疫情空间分布与景观生态风险指数和景观人工干扰强度均存在关联。[结论]研究期间瑞丽市景观类型变化明显,生态风险升高,生态环境出现恶化趋势;COVID-19疫情空间分布与景观格局具有相关性,城市建设用地和生态用地对病毒传播有空间聚集和阻碍的作用。

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.

The distribution and ecological risks of antibiotics in surface water in key cities along the lower reaches of the Yellow River:A case study of Kaifeng City,China

A large number of antibiotics have been discharged into rivers by human activities,posing a threat to aquatic ecosystems.The surface water of the Yellow River Basin also suffers antibiotic pollution,which hinders the improvement in the aquatic ecological environment.This study investigated and analyzed the characteristics and assessed the ecological risks of antibiotic pollution in surface water bodies such as canals,rivers and fish ponds in Kaifeng,Henan Province,which is a key city along the lower reaches of the Yellow River.The test results are as follows.A total of 15 types of antibiotics were detected in the surface water.They had a total antibiotic concentration of 12.2-249.9μg/L,of which tetracyclines(TCs)and quinolones accounted for the highest percentages.Six types of quinolones had detection rates of up to 100%,and doxycycline(DC)and oxytetracycline(OTC)had average concentrations of 29.52μg/L1 and 13.71μg/L,respectively.The major canals with water diverted from the Yellow River had total concentrations of quinolones and TCs of 22.0μg/L and 14.9μg/L,respectively,which were higher than those in previous studies.This phenomenon may be related to the decrease in the water flow of the Yellow River during the dry season and the increase in the antibiotic consumption of residents in the context of the Covid-19 outbreak.The upper reaches of the Huiji River in the Xiangfu District had higher antibiotic content than other districts in Kaifeng.Specifically,TCs accounted for 72.38%-91.84%of all antibiotics,and the DC and OTC concentrations were significantly higher than other antibiotics in the upper reaches.As indicated by the ecological risk assessment results,TCs had the highest ecological risks to green algae.Among them,DC had medium-high risks;TC,OTC,and chlortetracycline(CTC)had medium-high risks;trimethoprim(TMP)and lomefloxacin(LOM)had low risks;other TC antibiotics had no risk.Compared with green algae,most antibiotics showed higher ecological risks to daphnia and lower ecological risks to fish.DC and OTC dominate antibiotic pollutants in the surface water in Kaifeng City,and especially in Xiangfu District,where DC and OTC have medium-high risks.The TCs in the major Yellow River showed medium risks to both green algae and daphnia.It can be speculated that the antibiotic pollution in the Yellow River might pose a certain threat to the ecological security of water in Kaifeng City.

Impact of the COVID‑19 outbreak on the US equity sectors:Evidence from quantile return spillovers

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak.To this end,we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns.Notably,we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network.The results show that the network structure and spillovers differ considerably with respect to the market state.During stable times,the network shows a nice sectoral clustering structure which,however,changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure.The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated.The sectoral topology thus has not collapsed into a unified market during the pandemic.

Editorial commentary on special issue of COVID-19 pandemic

This special issue of The Journal of Biomedical Research presents rigorous empirical analysis related to COVID-19 research in responding to the current global COVID-19 pandemic.Selected articles from different disciplines not only offer broader perspectives on combating the outbreaks,but also disseminate the most updated findings on this new challenge for human being to the field.

Optimal control strategy for COVID-19 concerning both life and economy based on deep reinforcement learning

At present, the global COVID-19 is still severe. More and more countries have experienced second or even third outbreaks. The epidemic is far from over until the vaccine is successfully developed and put on the market on a large scale.Inappropriate epidemic control strategies may bring catastrophic consequences. It is essential to maximize the epidemic restraining and to mitigate economic damage. However, the study on the optimal control strategy concerning both sides is rare, and no optimal model has been built. In this paper, the Susceptible-Infectious-Hospitalized-Recovered(SIHR)compartment model is expanded to simulate the epidemic's spread concerning isolation rate. An economic model affected by epidemic isolation measures is established. The effective reproduction number and the eigenvalues at the equilibrium point are introduced as the indicators of controllability and stability of the model and verified the effectiveness of the SIHR model. Based on the Deep Q Network(DQN), one of the deep reinforcement learning(RL) methods, the blocking policy is studied to maximize the economic output under the premise of controlling the number of infections in different stages.The epidemic control strategies given by deep RL under different learning strategies are compared for different reward coefficients. The study demonstrates that optimal policies may differ in various countries depending on disease spread and anti-economic risk ability. The results show that the more economical strategy, the less economic loss in the short term,which can save economically fragile countries from economic crises. In the second or third outbreak stage, the earlier the government adopts the control strategy, the smaller the economic loss. We recommend the method of deep RL to specify a policy which can control the epidemic while making quarantine economically viable.