A Multivent System for Non-invasive Ventilation: Solving the Problem of Ventilator Shortage during the COVID-19 Pandemic

The COVID-19 pandemic has caused a severe global problem of ventilator shortage. Placing multiple patients on a single ventilator (ventilator sharing) or dual patient ventilation has been proposed and conducted to increase the cure efficiency for ventilated patients. However, the ventilator-sharing method needs to use the same ventilator settings for all the patients, which cannot meet the ventilation needs of different patients. Therefore, a novel multivent system for non-invasive ventilation has been proposed in this study. The close loop system consists of the proportional valve and the flow-pressure sensor can regulate the airway pressure and flow for each patient. Multiple ventilation circuits can be combined in parallel to meet patients’ventilation demands simultaneously. Meanwhile, the mathematical model of the multivent system is established and validated through experiments. The experiments for different inspired positive airway pressure (IPAP), expired positive airway pressure (EPAP), inspiratory expiratory ratio (I:E), and breath per minute (BPM) have been conducted and analyzed to test the performance of the multivent system. The results show that the multivent system can realize the biphasic positive airway pressure (BIPAP) ventilation mode in non-invasive ventilation without interfering among the three ventilation circuits, no matter the change of IPAP, EPAP, I:E, and BPM. However, pressure fluctuation exists during the ventilation process because of the exhaust valve effect, especially in EPAP control. The control accuracy and stability need to be improved. Nevertheless, the novel designed multivent system can theoretically solve the problem of ventilator shortage during the COVID-19 pandemic and may bring innovation to the current mechanical ventilation system. Author

Commodity supply risk assessment of China's copper industrial chain: The perspective of trade network

With the rapid development of China's new energy industry, the consumption demand for copper resources is increasing. As a key raw material, copper resources are becoming increasingly important. Taking the demand for copper commodities in China's new energy development as the research background and the international trade environment and pattern of copper supply as the research perspective, this paper makes an overall assessment of the commodity supply risk of China's copper industrial chain from 2010 to 2021 using the complex network and the newly established three-dimensional risk assessment model and finally reaches the following conclusions. The supply risk of commodities in China's copper industrial chain has been rising continuously since 2019 after experiencing fluctuating development in the early stage and a continuous decline in recent years, and there may be a trend of continuing to rise. The supply risk of China's copper industrial chain was gradually reduced from upstream to midstream and downstream, and the supply risk of copper smelting was more severe. The disruption potential risk of China's copper industrial chain was relatively low, and the international import market structure of copper commodities was relatively reasonable. The supply risk characteristics of each link in China's copper industrial chain were different. Due to the influence of import dependence, the copper mining industry had a high risk of trade exposure. However, the smelting and copper processing industries had certain limitations in production management, operation management and technology research and development, and their ability to withstand risks was weak. In addition, the impact of the domestic COVID-19 epidemic ha caused a high industrial chain vulnerability risk. © 2023 Elsevier Ltd

Financially insecure and less ethical: Understanding why and when financial insecurity inhibits ethical leadership

With the recent COVID-19 pandemic, among other crises (e.g., Russia–Ukraine conflicts and recession projections) threatening organizations' financial conditions across the globe, supervisors may not only encounter challenges such as job cuts that test their ethical leadership, but also experience financial insecurity themselves. However, our knowledge of why and when supervisors' ethical leadership behaviors may be affected in such a situation remains quite limited. In this research, we draw on uncertainty management theory (UMT) to examine the potential influence of financial insecurity on ethical leadership. Specifically, we suggest that financial insecurity triggers anxiety in supervisors, which inhibits their demonstration of ethical leadership. We also propose organizational pay fairness as a boundary condition for this process, such that supervisors who perceive their pay as fair are less susceptible to the anxiety resulting from financial insecurity than those who perceive their pay as unfair. Results from two multi-source, multi-wave studies supported our hypothesized model. We conclude by discussing the theoretical and practical implications of our findings. © The Author(s) 2023.

Mortality Prediction with Bidirectional Coupled and Gumbel Subset Network on Irregularly Multivariate Time Series

Mortality prediction is a crucial challenge because of multivariate time series (MTS) complexity, which are sparse, irregularly, asynchronous and hold missing values for various reasons in a single acquisition. Various methods are proposed to deal with missing values for the final mortality prediction. However, existing models only capture the temporal dependencies within a time series and are inefficient to capture the dependencies between time series to rebuild missing values for mortality prediction. To address these challenges, in this paper, we present an end-to-end imputation and mortality prediction model, named bidirectional coupled and Gumbel subset network (BiCGSN), for mortality prediction with such irregularly multivariate time series. Our proposed model (BiCGSN) uses a recurrent network to learn the temporal dependencies (intra-time series couplings) and uses a Gumbel selector on multi-head attention to obtain the relationship between the variables (inter-time series couplings) in the forward and backward directions. Then the learned bidirectional inter-and intra-time series couplings are fused to impute missing values for further mortality prediction. We evaluate our model on PhysioNet2012 and COVID-19 datasets to imputation and predict mortality. Experiments show that BiCGSN obtains the AUC 0.869 and 0.911 on two real-world datasets respectively and outperforms all the baselines. © 2022 IEEE.

Improving physical activity levels of children and adolescents is an important guarantee for Healthy China

The health of children and adolescents is of great significance to the realization of a "Healthy China". However, the current health problems of children and adolescents are showing a trend of frequent, high incidence and younger age. Complex and diverging characteristics of the environment, family and personal life behavior patterns contribute to risks and problems for children and adolescent health prevention and improvement. The outbreak and spread of COVID-19 epidemic has brought even more severe challenges to the health promotion of children and adolescents. In view of the connection between physical activity and the health benefits, consistent focus on "Physical Activity" and innovative ways and methods of health promotion through physical activity, can help provide an important guarantee for achieving the goal of "Healthy China". © 2022 Chinese Academy of Sciences. All rights reserved.

Purchase Intention in Agricultural Products Live-Streaming Commerce: A S-O-R Model

Agricultural products live-streaming commerce (APLC), a new form of e-commerce with unique features sinking to villages and towns, has significantly promoted the sales of agricultural products in China during COVID-19. However, limited research has explored how the unique and common live-streaming commerce features affect customer behavior in APLC. Drawing on stimulus-organism-response (S-O-R) model, this study develops a research framework to examine how the stimuli (practicality, interactivity, preferential, and commonweal) affect consumers’ purchase intention through the mediation of organism factors (perceived trust and perceived value) in ALPC context. 216 valid samples were collected via an online survey. SmartPLS3.0 was used to verify the research model and hypotheses. Findings indicate: perceived trust and perceived value positively affect purchase intention;practicality, interactivity, preferential, and commonweal are positively associated with perceived value;practicality and commonweal positively influence perceived trust;interactivity has no significant effect on perceived trust. Potential theoretical and practical contributions are discussed. © 2022, Springer Nature Switzerland AG.

“Scrambling”: Logic of horizontal competition between local governments based on three cases of interprovincial disaster counterpart support

Interprovincial counterpart support is a cooperative system used by local governments to achieve horizontal flow of resources based on cross-regional cooperation. Existing research has mainly focused on governance efficiency, institutional advantages, and ranking incentives while ignoring the scrambling behavior and operational mechanisms of local governments formed by ranking incentives and territorial responsibilities. This study selected the Wenchuan earthquake, Yushu earthquake, and COVID-19 as three typical cases. We constructed a theoretical framework for competition among provincial local governments and found that competition in interprovincial disaster counterpart support followed a dual behavioral logic of “striving to be first” and “fear of being last”. Specifically, local governments will choose striving to be first under the logic of time coercion, content games, and territorial responsibility;they will choose fear of being last under the logic of responsibility avoidance and moral pressure. This type of scrambling-based horizontal competition reflects the logic of local government competition tournaments. This study further revealed the specific processes, mechanisms, and results of horizontal local government competition, which can provide inspiration for cross-regional and provincial cooperation. © 2022 Shandong Normal University

SARS-CoV-2 Saliva Testing to Determine Viral Shedding Duration in Hemodialysis Patients

Background: Hemodialysis (HD) patients are vulnerable to COVID-19. Early detection of COVID-19 in dialysis clinics informs isolation and infection control policies. Saliva testing is an alternative to nasopharyngeal swab to detect SARS-CoV-2. The understanding of viral shedding in HD patients is limited. We explore viral shedding duration in HD patients and determine its correlation with immunosuppression. Method(s): Eligible patients diagnosed with COVID-19, confirmed by nasal swab RTPCR within 2 weeks of COVID-19 diagnosis, were recruited. They were given Salivette Saliva Collection kits and instructed to chew a cotton swab for 60 seconds. Result(s): 30 COVID-19 positive patients participated (Table 1). Each patient provided up to 7 saliva samples. 65 samples were collected for an average of 11+/-8 days (range 0-36) after diagnosis. 26 samples showed at least one COVID-19 target gene (N, ORF1ab) with cycle threshold <38 cycles. 12 patients had at least 1 positive sample, and 23 patients had at least 1 negative sample. Of the 23 patients who had at least one negative sample, median days to first negative sample is 9 days (range 0-36). For the 7 patients who only had positive samples, median days to last positive sample is 9 days (range 0-36). There is no observed difference between vaccinated (n=24) and vaccinated patients (n=6). 6 out of 30 patients took immunosuppressants such as Tacrolimus, Hydroxychloroquine, and Mycophenolate sodium. Median days to turn negative (or use last positive date if negative results never achieved) was 15 days for immunocompromised group and 8 days for nonimmunocompromised group (Fig.1) Conclusion(s): Immunocompromised HD patients shed COVID-19 virus for a significantly longer period. While our study did not explore the shedding of viable SARS-CoV-2, a longer isolation should be considered in immunosuppressed HD patients. Studies on shedding of viable SARS-CoV-2 are warranted in immunocompromised HD patients to inform policies regarding isolation and contact tracing protocols, and vaccination strategies.

Association Between Sleep and COVID-19 Vaccination Response in Hemodialysis Patients

Background: Hemodialysis (HD) patients are less likely to mount a response to the COVID-19 vaccination (CoVac). Poor sleep is associated with blunted vaccination response in the general population. We aim to explore the association between CoVac and sleep quality (SQ) in HD patients. Method(s): Patients from 3 HD clinics were enrolled if they were >=18 years and able to give written consent. Patients were administered the Insomnia Severity Index (ISI) and the Pittsburg Sleep Quality Index (PSQI). Blood specimen were collected after the primary series of COVID-19 vaccination. SARS-CoV-2 neutralization antibodies (nAB) were assayed using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (Cat#L00847-A). nAB titers are presented as Unit/ml on a natural log scale. PSQI scores of >5 were categorized as poor SQ and <=5 as good SQ. ISI scores were grouped as no clinically significant insomnia (NI;score 0-7), subthreshold insomnia (SI;score 8-14), and clinical insomnia (CI;score 14-28). T-test and ANOVA analysis were performed on PSQI and ISI scores, respectively, to determine the statistical association between SQ and nAB levels Results: 58 patients were included (60+/-9 years old, HD vintage 4.7+/-4.5 years, 62% male, 66% Black, 21% Hispanic). In the PSQI, 72% (n=42) had poor SQ. In the ISI, 52% = NI, 31% = SI, and 17% CI. Box plots of nAB levels with median and IQR are shown in Fig. 1. There is no association between SQ and nAB levels. Conclusion(s): There is no association between SQ and CoVac response. Given the immune dysfunction in this population, any modifying effect SQ has on CoVac, as observed in the general population, is unlikely. Other methods of improving CoVac response in this vulnerable population should be explored. (Figure Presented).

The Policy Choice and Economic Assessment of High Emissions Industries to Achieve the Carbon Peak Target under Energy Shortage—A Case Study of Guangdong Province

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. © 2022 by the authors.

[Progress in research of economic evaluation of non-pharmaceutical interventions for COVID-19 prevention and control].

Objective: To understand the research progresses of economic evaluation of non-pharmaceutical interventions (NPIs) both at home and abroad, and provide reference for economic evaluation of NPIs using real-world data in China. Methods: The literature retrieval was conducted by searching Chinese and English databases to indude papers about economic evaluation of NPIs and integrated NPIs published from January, 2020 to December, 2021, and the results were analyzed comprehensively. Results: A total of 30 Chinese and English literatures about economic evaluation of NPIs for COVID-19 prevention and control were included; including 7 papers about nucleic acid and testing and screening, 6 papers about individual prevention and protection measures, 12 papers about integrated implementation of individual prevention and protection, social distancing, nucleic acid or antigen testing, community screening and symptom screening, as well as close contact tracing and isolation/quarantine, and 5 papers about contain strategies, such as lockdown. This study found that personal protection, social distancing, and testing-tracing-isolation measures were cost-effective; however, different combinations of NPIs might lead to different results. Moreover, the cost of lockdown was high, which might cause huge economic burden. Conclusions: Most NPIs are cost-effective except lockdown, while the cost-effectiveness of the integrations of NPIs at different levels and in different scenarios needs to be further evaluated. It is necessary to carry out economic evaluation of integrated NPIs and the combination of NPIs with other interventions, such as vaccination and medication, based on real-world settings in China.

Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, we propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.

How Government Corruption and Market Segmentation Affect Green Total Factor Energy Efficiency in the Post-COVID-19 Era: Evidence From China

Energy and environmental pollution have attracted wide attention, but few studies have been conducted on green total factor energy efficiency (GTFEE) from the perspective of government corruption and market segmentation. By using the panel data of 30 provinces in China for the period 2006 to 2017, this paper tests the relationship between government corruption, market segmentation, and GTFEE. Moreover, considering the threshold effect of government corruption and market segmentation on GTFEE, the system generalized method of moments and the dynamic threshold panel model are adopted to analyze the nonlinear relationship. The regression results indicate that government corruption significantly decreases GTFEE, and market segmentation also has a significant negative impact on GTFEE. Moreover, market segmentation exacerbates the negative impact of corruption on GTFEE. The more serious the government corruption, the more severe the inhibitory effect of market segmentation on GTFEE. Similarly, the higher degree of market segmentation can increase the restraining effect of corruption on GTFEE. The results are still valid after a series of robustness tests. This paper suggests that countries should adopt severe anti-corruption actions, speed up the process of regional integration, and provide a good institutional environment support for the improvement of GTFEE. Copyright © 2022 Zhou, Du and Ren.

[Progress in research of economic evaluation of COVID-19 vaccination strategies].

Objective: To understand the research progresses of economic evaluation of COVID-19 vaccination strategies both at home and abroad, and provide reference for the economic evaluation of COVID-19 vaccination strategies using real word data in China. Methods: Literature retrieval was conducted for related papers published from January, 2020 to December, 2021 in Chinese and English databases, including the economic evaluation of COVID-19 vaccination, and the results of the related literatures were narratively integrated. Results: A total of 16 English literatures (including 3 reviews) were included, and it was found that the COVID-19 vaccination was cost-effective or cost-saving regardless of the vaccine types, while the cost-effectiveness in different population and under different vaccination dose strategies varied due to vaccine efficacy, vaccine price, duration of natural immunity, duration of vaccination campaign, vaccine supply, and vaccination pace. Conclusions: China lacks suitable evidences of economic evaluation of COVID-19 vaccination strategies based on real-world data in the context of long-term epidemic. Therefore, further researches of suitable strategies of booster COVID-19 vaccination are needed.

Multi-Media Content Clustering and Computer Intelligent Analysis by Text Mining

The epidemic situation of covid-19 spread all over the world, which is not optimistic. In order to extract valuable information for the epidemic from the numerous Internet data. With data mining technology, this paper crawls more than 10000 pieces of data from the microblog platform of overseas anti epidemic diary topic, and preprocesses the obtained text data set with word segmentation, removing stop words and other data, extracts the keywords of each microblog through word vector model, counts word frequency, and clustes text. In addition, the emotional value of the text is analyzed. Finally, the data were grouped into seven categories, and the trend chart of emotion value was drawn, and each result was displayed in the way of graph. By analysing, on the one hand, valuable information can be extracted from the micro blog data generated by overseas Chinese to help the domestic people understand the real situation of the overseas epidemic and adjust the risk response measures;on the other hand, the general situation of social media data during the epidemic can be generally understood from the macro perspective to provide reference for government departments in terms of management of entry-exit and epidemic prevention and control. It is helpful to further improve the governance system and the modernization of governance capacity in response to public health emergencies in China. © 2021 ACM.

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild. © 2022 Jianhong Lin et al.

Association of Race, Ethnicity, Age and Socioeconomic Status with Access to Virtual Visits within the Brigham & Women's Hospital Division of Hematology during the COVID-19 Pandemic

Background: The COVID-19 pandemic prompted an expedited shift towards expanding telemedicine services. Historically, telemedicine has been shown to increase healthcare access for those in rural communities but widen care gaps for other vulnerable populations by exacerbating existing digital divides and clinician biases in offering telemedicine services. The purpose of this study is to understand the demographic and socioeconomic characteristics of patients completing telephone, video, and in-person visits at the Brigham and Women's Hospital Division of Hematology (BWH DOH) during the COVID-19 pandemic. Methods: This was a retrospective chart review of patients who completed clinical encounters within the BWH DOH between March 19, 2020, when the division switched to virtual visits, and December 31 st, 2020 (pandemic). Patients who completed visits between January 1, 2019 and March 18, 2020 (pre-pandemic) served as a comparator group. Differences in socio-demographic composition of patients completing telephone only (TO), video only (VO), or a mix of in-person and telemedicine visits (IPTM) were tested for significance using Kruskal-Wallis and Chi-square tests. Results: A total of 8307 pre-pandemic visits and 5910 pandemic visits were included in analysis. Almost all visits (99.8%) were in-person pre-pandemic compared to 32.4% in-person, 42.6% by telephone, and 25% by video during pandemic. Median age was significantly different between patients who had only pre-pandemic visits, only pandemic visits, and both (55 vs 52 vs 58 years;p=0.003). Otherwise, there was no significant difference in racial and median income distributions pre-pandemic to pandemic. Table 1 shows the socio-demographic characteristics of patients who completed TO (1536), VO (1065), or IPTM (1518) visits during the pandemic. VO patients were significantly younger than TO and IPTM patients (p<0.001). The majority of patients identified as White (61.3%) with Black and Hispanic patients accounting for 13.8% and 11.4% of the pandemic population, respectively. A higher proportion of White patients had VO visits (29.9%) compared to Black (15.2%) and Hispanic patients (13%) who both had a higher proportion of TO visits (34.7% vs 40.4% vs 50.9%, p<0.001). More patients with a college (29.9%) or post-graduate (34.3%) degree had VO visits than patients with a high school (16.3%) or other levels of education (21.5%) who were more likely to have TO visits (p<0.001). Median household incomes approximated from patient zip codes were significantly higher in patients with VO visits than those with TO or IPTM visits (p<0.001). Discussion: This study shows that during the COVID-19 pandemic, there were significant differences in the socio-demographic composition of patients completing VO versus TO versus IPTM visits within the BWH DOH. Overall, individuals from groups that historically experience health inequities in the United States including the elderly, African Americans, Hispanics, and those with lower educational levels and socioeconomic status had fewer VO visits and more TO visits compared to patients who were younger, White, and had higher levels of education and socioeconomic status. These differences have important implications as VO visits may offer better clinical interaction when compared to TO visits. The younger age of patients seen during the pandemic compared to pre-pandemic suggests that some older adults lost access to hematology care altogether during the pandemic. This disparity pre-pandemic to pandemic was not observed in other demographic subsets. Our work reveals a need to focus on digital inclusion efforts centered around device access, internet access, and digital literacy. Additionally, while TO and VO visits are temporarily equally reimbursed as in person visits under the U.S government's COVID emergency declaration, there has already been a return to markedly lower reimbursement for TO visits. Many practices and hospital system have lost significant revenue due to the pandemic and this differential reimbursement may disincentivize provi ing care through TO, even if that is the patient's only means of access. This could pose as a further barrier to telemedicine access for individuals from vulnerable populations and exacerbate structural racism, ageism, and other inequities. Care must be taken moving forward that actions to cope with the pandemic or modernize health care serve all patients. [Formula presented] Disclosures: Neuberg: Pharmacyclics: Research Funding;Madrigal Pharmaceuticals: Other: Stock ownership. Achebe: Fulcrum Therapeutics: Consultancy;Pharmacosmos: Membership on an entity's Board of Directors or advisory committees;Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Fine-scale risk assessment of COVID-19 in Wuhan based on multisource geographical data

The severe outbreak of Coronavirus disease 2019 (COVID- 19) demonstrates the importance of disease risk assessment. The existing risk assessment methods are limited by the real time and accuracy of data. Most of them take the administrative statistical unit as the analysis scale, which has modifiable areal unit problem (MAUP) . First, based on a random forest method, we integrated COVID-19 transmission data at community scale and multisource geospatial data to map COVID-19 disease outbreak risks at fine scale. The experimental results (overall accuracy = 0.85, Kappa = 0.70) indicated the feasibility of the model. Second, we built a spatial variable-infection risk model at community and place scale to assess the risk degree of epidemic spread in different places and facilities. Last, we analyzed the possibly spatial drivers of disease transmission. The results show that (1) the central area of Wuhan city has the highest risk of infection and the risk map presents a trend of decreasing from the center to the periphery;(2) The top five facilities with the highest risk of COVID- 19 infection are shopping, medical, financial, transportation and public facilities;(3) The transmission risk of the epidemic is low in primary and middle schools, but high in colleges and universities;(4) The model determines the degree of epidemic risk at the community scale and predicts that shopping and traffic places are two most significant driving factors with the epidemic outbreak. In conclusion, this study suggests a new method of disease risk assessment based on a fine scale, which can pave the way for future disease risk assessment. © 2021 Science Press. All rights reserved.

Predicting Interactions Between Pathogen and Human Proteins Based on the Relation Between Sequence Length and Amino Acid Composition

Aim: Both bacterial infection and viral infection involve a large number of protein-protein interactions (PPIs) between a pathogen and its target host. Background: So far, many computational methods have focused on predicting PPIs within the same species rather than PPIs across different species. Methods: From the extensive analysis of PPIs between Yersinia pestis bacteria and humans, we recently discovered an interesting relation;a linear relation between amino acid composition and sequence length was observed in many proteins involved in PPIs. We have built a support vector machine (SVM) model, which predicts PPIs between human and bacteria using two feature types derived from the relation. The two feature types used in the SVM are the amino acid composition group (AACG) and the difference in amino acid composition between host and pathogen proteins. Results: The SVM model achieved high performance in predicting bacteria-human PPIs. The model showed an accuracy of 96%, sensitivity of 94%, and specificity of 98% in predicting PPIs between humans and Yersinia pestis, in which there is a strong relation between amino acid composition and sequence length. The SVM model was also tested in predicting PPIs between human and viruses, which include Ebola, HCV, and SARS-CoV-2, and showed a good performance. Conclusion: The feature types identified in our study are simple yet powerful in predicting pathogenhuman PPIs. Although preliminary, our method will be useful for finding unknown target host proteins or pathogen proteins and designing in vitro or in vivo experiments.