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1.
6th International Conference on Big Data and Internet of Things, BDIOT 2022 ; JOUR: 20-26,
Article in English | Scopus | ID: covidwho-2088937

ABSTRACT

Accurate prediction of 2019 novel coronavirus diseases (COVID-19) has been playing an important role in making more effective prevention and control policies during pandemic crises. The aim of this paper was to develop an innovative stacking based prediction of COVID-19 pandemic cumulative confirmed cases (StackCPPred) by integrating infectious disease dynamics model and traditional machine learning. Based on population migration characteristics, five feature indicators were first extracted from the population flow data in the early stage of this epidemic, which were collected from the National Health Commission of the People's Republic of China. Then, stacking based ensemble learning (SEL) model was established for COVID-19 prediction using traditional machine learning, including the multiple linear regression (MLR) and the tree regression model (XGBoost and LightGBM). By introducing the variable "death state", an improved Susceptible-Infected-Recovered (ISIR) model was established. Finally, a hybrid model, StackCPPred was proposed by incorporating the ISIR model outputs and the five feature indicators into the SEL model. Real data on population movements and daily cumulative number of newly confirmed cases across the country from January 23 to February 6 were used to validate our model. The results positively proved that the proposed StackCPPred model outperformed the existing models for COVID-19 prediction, as quantified by the root mean square error (RMSE), the root mean square logarithmic error (RMSLE) and the coefficient of determination (R2) (g1/41841 persons, g1/40.1 and >0.9, respectively). Furthermore, this study confirms the validity and usefulness of the StackCPPred model for COVID-19 prediction. © 2022 ACM.

2.
16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2078231

ABSTRACT

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction. © 2022 IEEE.

3.
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 ; : 2882-2892, 2022.
Article in English | Scopus | ID: covidwho-2020398

ABSTRACT

To control the outbreak of COVID-19, efficient individual mobility intervention for EPidemic Control (EPC) strategies are of great importance, which cut off the contact among people at epidemic risks and reduce infections by intervening the mobility of individuals. Reinforcement Learning (RL) is powerful for decision making, however, there are two major challenges in developing an RL-based EPC strategy: (1) the unobservable information about asymptomatic infections in the incubation period makes it difficult for RL's decision-making, and (2) the delayed rewards for RL causes the deficiency of RL learning. Since the results of EPC are reflected in both daily infections (including unobservable asymptomatic infections) and long-term cumulative cases of COVID-19, it is quite daunting to design an RL model for precise mobility intervention. In this paper, we propose a Variational hiErarcHICal reinforcement Learning method for Epidemic control via individual-level mobility intervention, namely Vehicle. To tackle the above challenges, Vehicle first exploits an information rebuilding module that consists of a contact-risk bipartite graph neural network and a variational LSTM to restore the unobservable information. The contact-risk bipartite graph neural network estimates the possibility of an individual being an asymptomatic infection and the risk of this individual spreading the epidemic, as the current state of RL. Then, the Variational LSTM further encodes the state sequence to model the latency of epidemic spreading caused by unobservable asymptomatic infections. Finally, a Hierarchical Reinforcement Learning framework is employed to train Vehicle, which contains dual-level agents to solve the delayed reward problem. Extensive experimental results demonstrate that Vehicle can effectively control the spread of the epidemic. Vehicle outperforms the state-of-the-art baseline methods with remarkably high-precision mobility interventions on both symptomatic and asymptomatic infections. © 2022 Owner/Author.

4.
Cancer Research ; 82(12), 2022.
Article in English | EMBASE | ID: covidwho-1986483

ABSTRACT

Recent clinical observations that some coronavirus infections induced complete remissions in lymphoma patients emphasized again the potential of cancer virotherapy. Infection of cancer cells with oncolytic viruses reshapes the tumor microenvironment by activating anti-viral and anti-tumor immunity. A phase 1 clinical trial using oncolytic adenovirus Delta-24-RGD (DNX-2401) to treat recurrent malignant gliomas demonstrated activation of CD8+ T-cells and significant clinical benefits for a subset of patients. However, both anti-virus and anti-tumor immune responses are contingent on the activation of respective clones of CD8+ T-cells, which compete for clonal expansion. Thus, overexpansion of T-cells against viral antigens reduces the frequency of subdominant clones against tumor antigens. We hypothesized that inducing immune tolerance for viral antigens will decrease anti-viral immunity and in turn derepress anti-tumor immunity, resulting in enhanced efficacy of cancer virotherapy. In this work, we used nanoparticles encapsulating adenoviral antigens E1A, E1B and hexon that distributed to liver resident macrophages (P<0.0001) and induced peripheral immune tolerance. Functional experiments to restimulate immune cells with viral or tumor antigens showed that injection of nanoparticles induced virus-specific immune tolerance and redirected the focus of the immune response towards tumor peptides as measured by interferon-gamma secretion (P<0.0001). Co-culture experiments also showed increased activation of immune cells against fixed tumor cells after nanoparticle treatment (P<0.0001). Reduction of virus-specific T-cells and concurrent expansion of tumor-specific T-cell clones were further confirmed with E1A or OVA tetramers (P<0.05). Flow cytometry analysis suggested increased anti-tumor responses were due to differences in T-cell clones and not due to other immune populations including natural killer cells or myeloid-derived suppressor cells (P=0.3). Importantly, virotherapy in combination with nanoparticle-induced immune tolerance towards viral antigens in tumor-bearing mice increased the overall survival and doubled the percentage of long-term survivors compared to virus treatment alone. Our data should propel the development of a future clinical trial aiming to maximize the potential of anti-tumor immunity during cancer virotherapies.

5.
Stud Mycol ; 101: 417-564, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1902874

ABSTRACT

This paper is the fourth contribution in the Genera of Phytopathogenic Fungi (GOPHY) series. The series provides morphological descriptions and information about the pathology, distribution, hosts and disease symptoms, as well as DNA barcodes for the taxa covered. Moreover, 12 whole-genome sequences for the type or new species in the treated genera are provided. The fourth paper in the GOPHY series covers 19 genera of phytopathogenic fungi and their relatives, including Ascochyta, Cadophora, Celoporthe, Cercospora, Coleophoma, Cytospora, Dendrostoma, Didymella, Endothia, Heterophaeomoniella, Leptosphaerulina, Melampsora, Nigrospora, Pezicula, Phaeomoniella, Pseudocercospora, Pteridopassalora, Zymoseptoria, and one genus of oomycetes, Phytophthora. This study includes two new genera, 30 new species, five new combinations, and 43 typifications of older names. Taxonomic novelties: New genera: Heterophaeomoniella L. Mostert, C.F.J. Spies, Halleen & Gramaje, Pteridopassalora C. Nakash. & Crous; New species: Ascochyta flava Qian Chen & L. Cai, Cadophora domestica L. Mostert, R. van der Merwe, Halleen & Gramaje, Cadophora rotunda L. Mostert, R. van der Merwe, Halleen & Gramaje, Cadophora vinacea J.R. Úrbez-Torres, D.T. O'Gorman & Gramaje, Cadophora vivarii L. Mostert, Havenga, Halleen & Gramaje, Celoporthe foliorum H. Suzuki, Marinc. & M.J. Wingf., Cercospora alyssopsidis M. Bakhshi, Zare & Crous, Dendrostoma elaeocarpi C.M. Tian & Q. Yang, Didymella chlamydospora Qian Chen & L. Cai, Didymella gei Qian Chen & L. Cai, Didymella ligulariae Qian Chen & L. Cai, Didymella qilianensis Qian Chen & L. Cai, Didymella uniseptata Qian Chen & L. Cai, Endothia cerciana W. Wang. & S.F. Chen, Leptosphaerulina miscanthi Qian Chen & L. Cai, Nigrospora covidalis M. Raza, Qian Chen & L. Cai, Nigrospora globospora M. Raza, Qian Chen & L. Cai, Nigrospora philosophiae-doctoris M. Raza, Qian Chen & L. Cai, Phytophthora transitoria I. Milenkovic, T. Májek & T. Jung, Phytophthora panamensis T. Jung, Y. Balci, K. Broders & I. Milenkovic, Phytophthora variabilis T. Jung, M. Horta Jung & I. Milenkovic, Pseudocercospora delonicicola C. Nakash., L. Suhaizan & I. Nurul Faziha, Pseudocercospora farfugii C. Nakash., I. Araki, & Ai Ito, Pseudocercospora hardenbergiae Crous & C. Nakash., Pseudocercospora kenyirana C. Nakash., L. Suhaizan & I. Nurul Faziha, Pseudocercospora perrottetiae Crous, C. Nakash. & C.Y. Chen, Pseudocercospora platyceriicola C. Nakash., Y. Hatt, L. Suhaizan & I. Nurul Faziha, Pseudocercospora stemonicola C. Nakash., Y. Hatt., L. Suhaizan & I. Nurul Faziha, Pseudocercospora terengganuensis C. Nakash., Y. Hatt., L. Suhaizan & I. Nurul Faziha, Pseudocercospora xenopunicae Crous & C. Nakash.; New combinations: Heterophaeomoniella pinifoliorum (Hyang B. Lee et al.) L. Mostert, C.F.J. Spies, Halleen & Gramaje, Pseudocercospora pruni-grayanae (Sawada) C. Nakash. & Motohashi., Pseudocercospora togashiana (K. Ito & Tak. Kobay.) C. Nakash. & Tak. Kobay., Pteridopassalora nephrolepidicola (Crous & R.G. Shivas) C. Nakash. & Crous, Pteridopassalora lygodii (Goh & W.H. Hsieh) C. Nakash. & Crous; Typification: Epitypification: Botrytis infestans Mont., Cercospora abeliae Katsuki, Cercospora ceratoniae Pat. & Trab., Cercospora cladrastidis Jacz., Cercospora cryptomeriicola Sawada, Cercospora dalbergiae S.H. Sun, Cercospora ebulicola W. Yamam., Cercospora formosana W. Yamam., Cercospora fukuii W. Yamam., Cercospora glochidionis Sawada, Cercospora ixorana J.M. Yen & Lim, Cercospora liquidambaricola J.M. Yen, Cercospora pancratii Ellis & Everh., Cercospora pini-densiflorae Hori & Nambu, Cercospora profusa Syd. & P. Syd., Cercospora pyracanthae Katsuki, Cercospora horiana Togashi & Katsuki, Cercospora tabernaemontanae Syd. & P. Syd., Cercospora trinidadensis F. Stevens & Solheim, Melampsora laricis-urbanianae Tak. Matsumoto, Melampsora salicis-cupularis Wang, Phaeoisariopsis pruni-grayanae Sawada, Pseudocercospora angiopteridis Goh & W.H. Hsieh, Pseudocercospora basitruncata Crous, Pseudocercospora boehmeriigena U. Braun, Pseudocercospora coprosmae U. Braun & C.F. Hill, Pseudocercospora cratevicola C. Nakash. & U. Braun, Pseudocercospora cymbidiicola U. Braun & C.F. Hill, Pseudocercospora dodonaeae Boesew., Pseudocercospora euphorbiacearum U. Braun, Pseudocercospora lygodii Goh & W.H. Hsieh, Pseudocercospora metrosideri U. Braun, Pseudocercospora paraexosporioides C. Nakash. & U. Braun, Pseudocercospora symploci Katsuki & Tak. Kobay. ex U. Braun & Crous, Septogloeum punctatum Wakef.; Neotypification: Cercospora aleuritis I. Miyake; Lectotypification: Cercospora dalbergiae S.H. Sun, Cercospora formosana W. Yamam., Cercospora fukuii W. Yamam., Cercospora glochidionis Sawada, Cercospora profusa Syd. & P. Syd., Melampsora laricis-urbanianae Tak. Matsumoto, Phaeoisariopsis pruni-grayanae Sawada, Pseudocercospora symploci Katsuki & Tak. Kobay. ex U. Braun & Crous. Citation: Chen Q, Bakhshi M, Balci Y, Broders KD, Cheewangkoon R, Chen SF, Fan XL, Gramaje D, Halleen F, Horta Jung M, Jiang N, Jung T, Májek T, Marincowitz S, Milenkovic T, Mostert L, Nakashima C, Nurul Faziha I, Pan M, Raza M, Scanu B, Spies CFJ, Suhaizan L, Suzuki H, Tian CM, Tomsovský M, Úrbez-Torres JR, Wang W, Wingfield BD, Wingfield MJ, Yang Q, Yang X, Zare R, Zhao P, Groenewald JZ, Cai L, Crous PW (2022). Genera of phytopathogenic fungi: GOPHY 4. Studies in Mycology 101: 417-564. doi: 10.3114/sim.2022.101.06.

6.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333618

ABSTRACT

BACKGROUND: Acute and chronic alcohol abuse have adverse impacts on both the innate and adaptive immune response, which may result in reduced resistance to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and promote the progression of coronavirus disease 2019 (COVID-19). However, there are no large population-based data evaluating potential causal associations between alcohol consumption and COVID-19. METHOD: We conducted a Mendelian randomization study using data from UK Biobank to explore the association between alcohol consumption and risk of SARS-CoV-2 infection and serious clinical outcomes in patients with COVID-19. A total of 12,937 participants aged 50-83 who tested for SARS-CoV-2 between 16 March to 27 July 2020 (12.1% tested positive) were included in the analysis. The exposure factor was alcohol consumption. Main outcomes were SARS-CoV-2 positivity and death in COVID-19 patients. We generated weighted and unweighted allele scores using three genetic variants (rs1229984, rs1260326, and rs13107325) and applied the allele scores as the instrumental variables to assess the effect of alcohol consumption on outcomes. Analyses were conducted separately for white participates with and without obesity. RESULTS: Of the 12,937 participants, 4,496 were never or infrequent drinkers and 8,441 were frequent drinkers. (including 1,156 light drinkers, 3,795 moderate drinkers, and 3,490 heavy drinkers). Both logistic regression and Mendelian randomization analyses found no evidence that alcohol consumption was associated with risk of SARS-CoV-2 infection in participants either with (OR=0.963, 95%CI 0.800-1.159;q =1.000) or without obesity (OR=0.891, 95%CI 0.755-1.053;q =.319). However, frequent drinking (HR=1.565, 95%CI 1.012-2.419;q =.079), especially heavy drinking (HR=2.071, 95%CI 1.235-3.472;q =.054), was associated with higher risk of death in patients with obesity and COVID-19, but not in patients without obesity. Notably, the risk of death in frequent drinkers with obesity increased slightly with the average amount of alcohol consumed weekly (HR=1.480, 95%CI 1.059-2.069;q =.099). CONCLUSIONS: Our findings suggested alcohol consumption may had adverse effects on the progression of COVID-19 in white participants with obesity, but was not associate with susceptibility to SARS-CoV-2 infection.

7.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333576

ABSTRACT

SARS-CoV-2 is a novel coronavirus which has caused the COVID-19 pandemic. Other known coronaviruses show a strong pattern of seasonality, with the infection cases in humans being more prominent in winter. Although several plausible origins of such seasonal variability have been proposed, its mechanism is unclear. SARS-CoV-2 is transmitted via airborne droplets ejected from the upper respiratory tract of the infected individuals. It has been reported that SARS-CoV-2 can remain infectious for hours on surfaces. As such, the stability of viral particles both in liquid droplets as well as dried on surfaces is essential for infectivity. Here we have used atomic force microscopy to examine the structural stability of individual SARS-CoV-2 virus like particles at different temperatures. We demonstrate that even a mild temperature increase, commensurate with what is common for summer warming, leads to dramatic disruption of viral structural stability, especially when the heat is applied in the dry state. This is consistent with other existing non-mechanistic studies of viral infectivity, provides a single particle perspective on viral seasonality, and strengthens the case for a resurgence of COVID-19 in winter. STATEMENT OF SCIENTIFIC SIGNIFICANCE: The economic and public health impact of the COVID-19 pandemic are very significant. However scientific information needed to underpin policy decisions are limited partly due to novelty of the SARS-CoV-2 pathogen. There is therefore an urgent need for mechanistic studies of both COVID-19 disease and the SARS-CoV-2 virus. We show that individual virus particles suffer structural destabilization at relatively mild but elevated temperatures. Our nanoscale results are consistent with recent observations at larger scales. Our work strengthens the case for COVID-19 resurgence in winter.

8.
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 ; : 832-837, 2021.
Article in English | Scopus | ID: covidwho-1706842

ABSTRACT

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model. © 2021 IEEE.

9.
MEDLINE;
Preprint in English | MEDLINE | ID: ppcovidwho-327320

ABSTRACT

One major limitation of neutralizing antibody-based COVID-19 therapy is the requirement of costly cocktails to reduce antibody resistance. We engineered two bispecific antibodies (bsAbs) using distinct designs and compared them with parental antibodies and their cocktail. Single molecules of both bsAbs block the two epitopes targeted by parental antibodies on the receptor-binding domain (RBD). However, bsAb with the IgG-(scFv) 2 design (14-H-06) but not the CrossMAb design (14-crs-06) increases antigen-binding and virus-neutralizing activities and spectrum against multiple SARS-CoV-2 variants including the Omicron, than the cocktail. X-ray crystallography and computational simulations reveal distinct neutralizing mechanisms for individual cocktail antibodies and suggest higher inter-spike crosslinking potentials by 14-H-06 than 14-crs-06. In mouse models of infections by SARS-CoV-2 and the Beta, Gamma, and Delta variants, 14-H-06 exhibits higher or equivalent therapeutic efficacy than the cocktail. Rationally engineered bsAbs represent a cost-effective alternative to antibody cocktails and a promising strategy to improve potency and breadth.

10.
33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021 ; 2021-November:841-845, 2021.
Article in English | Scopus | ID: covidwho-1685095

ABSTRACT

Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) algorithms have been widely discussed by the Explainable AI (XAI) community but their application to wider domains are rare, potentially due to the lack of easy-to-use tools built around these methods. In this paper, we present ExMed, a tool that enables XAI data analytics for domain experts without requiring explicit programming skills. It supports data analytics with multiple feature attribution algorithms for explaining machine learning classifications and regressions. We illustrate its domain of applications on two real world medical case studies, with the first one analysing COVID-19 control measure effectiveness and the second one estimating lung cancer patient life expectancy from the artificial Simulacrum health dataset. We conclude that ExMed can provide researchers and domain experts with a tool that both concatenates flexibility and transferability of medical sub-domains and reveal deep insights from data. © 2021 IEEE.

11.
29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 ; : 240-243, 2021.
Article in English | Scopus | ID: covidwho-1528576

ABSTRACT

The coronavirus disease 2019 (COVID-19) break-out in late December 2019 has spread rapidly worldwide. Existing studies have shown that there is a significant correlation between large-scale human movements and the spread of the epidemic. However, there is a lack of quantification of these correlations, and it is still challenging to predict the spread of the epidemic at early stage. In this paper, we address this issue by conducting a statistical analysis on the spatio-temporal relationship between human mobility and the epidemic spread. Specifically, we proposed an improved SEIR model to adapt to the COVID-19 epidemic, so that we can predict the spread of the epidemic at the early stage using human mobility data and the early confirmed cases. We evaluated our model in various provinces and cities in China, and the results are superior to various baselines, verifying the effectiveness of the method. © 2021 ACM.

12.
18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021 ; 13031 LNAI:323-335, 2021.
Article in English | Scopus | ID: covidwho-1525495

ABSTRACT

From a dataset, one can construct different machine learning (ML) models with different parameters and/or inductive biases. Although these models give similar prediction performances when tested on data that are currently available, they may not generalise equally well on unseen data. The existence of multiple equally performing models exhibits underspecification of the ML pipeline used for producing such models. In this work, we propose identifying underspecification using feature attribution algorithms developed in Explainable AI. Our hypothesis is: by studying the range of explanations produced by ML models, one can identify underspecification. We validate this by computing explanations using the Shapley additive explainer and then measuring statistical correlations between them. We experiment our approach on multiple datasets drawn from the literature, and in a COVID-19 virus transmission case study. © 2021, Springer Nature Switzerland AG.

13.
Kexue Tongbao/Chinese Science Bulletin ; 66(31):3925-3931, 2021.
Article in Chinese | Scopus | ID: covidwho-1523391

ABSTRACT

Left unmitigated, climate change poses a catastrophic risk to human health, demanding an urgent and concerted response from every country. The 2015 Lancet Commission on Health and Climate Change and The Lancet Countdown: Tracking Progress on Health and Climate Change have been initiated to map out the impacts of climate change and the necessary policy responses. To meet these challenges, Tsinghua University, partnering with the University College London and 17 Chinese and international institutions, has prepared the Chinese Lancet Countdown report, which has a national focus and builds on the work of the global Lancet Countdown: Tracking Progress on Health and Climate Change. Drawing on international methodologies and frameworks, this report aims to deepen the understanding of the links between public health and climate change at the national level and track them with 23 indicators. This work is part of the Lancet's Countdown broader efforts to develop regional expertise on this topic, and coincides with the launch of the Lancet Countdown Regional Centre in Asia, based at Tsinghua University. The data and results of this report are presented at the provincial level, where possible, to facilitate targeted response strategies for local decision-makers. Based on the data and findings of the 2020 Chinese Lancet Countdown report, five recommendations are proposed to key stakeholders in health and climate change in China: (1) Enhance inter-departmental cooperation. Climate change is a challenge that demands an integrated response from all sectors, urgently requiring substantial inter-departmental cooperation among health, environment, energy, economic, financial, and education authorities. (2) Strengthen health emergency preparedness. Knowledge and findings on current and future climate-related health threats still lack the required attention and should be fully integrated into the emergency preparedness and response system. (3) Support research and raise awareness. Additional financial support should be allocated to health and climate change research in China to enhance health system adaptation, mitigation measures, and their health benefits. At the same time, media and academia should be fully motivated to raise the public and politicians' awareness of this topic. (4) Increase climate change mitigation. Speeding up the phasing out of coal is necessary to be consistent with China's pledge to be carbon neutral by 2060 and to continue to reduce air pollution. Fossil fuel subsidies must also be phased out. (5) Ensure the recovery from COVID-19 to protect health now and in the future. China's efforts to recover from COVID-19 will shape public health for years to come. Climate change should be a priority in these interventions. © 2021, Science Press. All right reserved.

14.
IEEE Transactions on Automation Science and Engineering ; 2021.
Article in English | Scopus | ID: covidwho-1515168

ABSTRACT

This article addresses a weekly physician scheduling problem in Covid-19. This problem has arisen in fever clinics in two collaborative hospitals located in Shanghai, China. Because of the coronavirus pandemic, the hospitals must consider some specific constraints in the scheduling problem. For example, due to social distance limitation, the patient queue lengths are much longer in the coronavirus pandemic, even with the same waiting patients. Thus, the hospitals must consider the maximum queue length in the physician scheduling problem. Moreover, the fever clinic's scheduling rules are different from those in the common clinic, and some specific regulatory constraints have to be considered in the epidemic. We first build a mathematical model for this problem, in which a pointwise stationary fluid flow approximation method is used to compute the queue length. Some linearization techniques are designed to make the problem can be solved by commercial solvers, such as Gurobi. We find that solving this model from practical applications of the hospital within an acceptable computation time is challenging. Consequently, we develop an efficient two-phase approach to solve the problem. A staffing model and a branch-and-price algorithm are proposed in this approach. The performances of our models and approaches are discussed. The effectiveness of the proposed algorithms for real-life data from collaborative hospitals is validated. IEEE

15.
Chinese Journal of Endocrinology and Metabolism ; 37(7):631-636, 2021.
Article in Chinese | Scopus | ID: covidwho-1362632

ABSTRACT

Objective: To analyze the correlation between serum uric acid and clinical features of patients with novel coronavirus pneumonia(COVID-19). Methods: A total of 200 patients with COVID-19 admitted to Wuhan Lei Shen Shan hospital from January 20, 2020 to April 10, 2020 were included in this retrospective cohort study. The patients were divided into the hyperuricemia group and the non-hyperuricemia group. The data of patients were collected through electronic medical record system. SPSS 19.0 and Graphpad Prism 8.0 statistical software were used to compare clinical features, laboratory results, survival time, and prognosis of patients between hyperuricemia and non-hyperuricemia groups. Results: Compared with the non-hyperuricemia group, the hyperuricemia group showed a higher BMI and mortality(P <0.05)as well as higher white blood cell count, lymphocytes, serum creatinine, creatine kinase, cystatin C, myoglobin, interleukin(IL)-6 levels(P <0.05). Serum uric acid level was positively correlated with lymphocytes, hemoglobin, albumin, creatinine, creatine kinase, cystatin C, D-dimer levels while negatively correlated with IL-2 receptor and IL-8. The patients with hyperuricemia had significantly shorter survival time and worse prognosis than those without hyperuricemia(P =0.04). Conclusion: COVID-19 patients with hyperuricemia show higher mortality and worse prognosis compared with the patients with non-hyperuricemia. Copyright © 2021 by the Chinese Medical Association.

16.
Jundishapur Journal of Microbiology ; 14(2), 2021.
Article in English | EMBASE | ID: covidwho-1359387

ABSTRACT

Introduction: Mycobacterium mucogenicum belongs to the rapidly growing mycobacteria, and it is a rare conditional pathogen. Although recent studies suggested that the incidence of M. mucogenicum infection was increased worldwide, there are no case reports of M. mucogenicum and Klebsiella pneumoniae pulmonary infection. Case Presentation: A 32-year-old non-smoking male was diagnosed with congenital atrial septal defect and pulmonary arterial hypertension. After cardiac surgery, lung infections were observed in the patient and then rapidly developed acute respiratory distress syndrome. The cefoperazone and sulbactam, vancomycin, ceftazidime, carbapenem, tigecycline, and micafungin were used for the treatment of pulmonary infection but did not affect. Ultimately, M. mucogenicum and K. pneumoniae were identified as pathogens by using next-generation sequencing. The patient was treated successfully with the administration of clarithromycin, linezolid, tigecycline, and ceftazidime-avibactam. The clinical outcome of this patient was favorable without relapse of infection. Conclusions: This case demonstrates that M. mucogenicum pulmonary infection may result in severe outcomes. The next-generation sequencing technology is important for the identification of M. mucogenicum. Additionally, the clinicians and clinical pharmacists should remain awareness in dealing with M. mucogenicum infection to avoid delaying appropriate treatment.

17.
Public Health ; 198: 1-5, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1331158

ABSTRACT

OBJECTIVES: As a unique prevention and control measure, the dispatch of national medical teams to Wuhan has played a key role in protecting Wuhan against COVID-19. This study aimed to quantitatively evaluate the effect of this key measure in reducing infections and fatalities. STUDY DESIGN: A scenario analysis is used in this study, where the forming of scenarios is on the basis of the stages of medical to Wuhan. We divided the evaluation into 4 scenarios: Scenario Ⅰ-no dispatch, Scenario Ⅱ-dispatch of 4599 medical staff, Scenario Ⅲ-dispatch of 16,000 staff, and Scenario Ⅳ-dispatch of 32,000 staff. METHODS: The extended Susceptible-Exposed-Infectious-Recovered-Death model was adopted to quantify the effect of the dispatch of national medical teams to Wuhan on COVID-19 prevention and control. RESULTS: The dispatch dramatically cuts the channels for the transmission of the virus and succeeds in raising the cure rates while reducing the fatality rates. If there were no dispatch at all, a cumulative total of 158,881 confirmed cases, 18,700 fatalities and a fatality rate of 11.77% would have occurred in Wuhan, which are 3.2 times, 4.8 times and 1.5 times the real figures respectively. The dispatch has avoided 108,541 confirmed cases and 14,831 fatalities in this city. CONCLUSIONS: The proven successful measure provides valuable experience and enlightenment to international cooperation on prevention and control of COVID-19, as well as a similar outbreak of new emerging infectious diseases.


Subject(s)
COVID-19 , China/epidemiology , Disease Outbreaks , Humans , SARS-CoV-2
18.
Ieee Access ; 9:96964-96974, 2021.
Article in English | Web of Science | ID: covidwho-1327483

ABSTRACT

Coronavirus disease 2019 has seriously affected the world. One major protective measure for individuals is to wear masks in public areas. Several regions applied a compulsory mask-wearing rule in public areas to prevent transmission of the virus. Few research studies have examined automatic face mask detection based on image analysis. In this paper, we propose a deep learning based single-shot light-weight face mask detector to meet the low computational requirements for embedded systems, as well as achieve high performance. To cope with the low feature extraction capability caused by the light-weight model, we propose two novel methods to enhance the model's feature extraction process. First, to extract rich context information and focus on crucial face mask related regions, we propose a novel residual context attention module. Second, to learn more discriminating features for faces with and without masks, we introduce a novel auxiliary task using synthesized Gaussian heat map regression. Ablation studies show that these methods can considerably boost the feature extraction ability and thus increase the final detection performance. Comparison with other models shows that the proposed model achieves state-of-the-art results on two public datasets, the AIZOO and Moxa3K face mask datasets. In particular, compared with another light-weight you only look once version 3 tiny model, the mean average precision of our model is 1.7% higher on the AIZOO dataset, and 10.47% higher on the Moxa3K dataset. Therefore, the proposed model has a high potential to contribute to public health care and fight against the coronavirus disease 2019 pandemic.

19.
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics ; 47(3):658-664, 2021.
Article in Chinese | Scopus | ID: covidwho-1200375

ABSTRACT

Multimodal deformable registration is designed to solve dense spatial transformations and is used to align images of two different modalities. It is a key issue in many medical image analysis applications. Multimodal image registration based on traditional methods aims to solve the optimization problem of each pair of images, and usually achieves excellent registration performance, but the calculation cost is high and the running time is long. The deep learning method greatly reduces the running time by learning the network used to perform registration. These learning-based methods are very effective for single-modality registration. However, the intensity distribution of different modal images is unknown and complex. Most existing methods rely heavily on label data. Faced with these challenges, this paper proposes a deep multimodal registration framework based on unsupervised learning. Specifically, the framework consists of feature learning based on matching amount and deformation field learning based on maximum posterior probability, and realizes unsupervised training by means of spatial conversion function and differentiable mutual information loss function. In the 3D image registration tasks of MRI T1, MRI T2 and CT, the proposed method is compared with the existing advanced multi-modal registration methods. In addition, the registration performance of the proposed method is demonstrated on the latest COVID-19 CT data. A large number of results show that the proposed method has a competitive advantage in registration accuracy compared with other methods, and greatly reduces the calculation time. © 2021, Editorial Board of JBUAA. All right reserved.

20.
Mathematics ; 9(5):1-23, 2021.
Article in English | Scopus | ID: covidwho-1138741

ABSTRACT

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best-worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio- economic development situation. The BBWM is used for the weights’ determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to “Medium” grade, but closer to “High” grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment;and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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