Identifying the Superspreader in Proactive Backward Contact Tracing by Deep Learning

The goal of proactive contact tracing is to diminish the spread of an epidemic by means of contact tracing mobile apps and big data analysis. Finding superspreaders as has been used in Japan and Australia during the early days of the COVID-19 pandemic has proven effective as backward contact tracing can pick up infections that might otherwise be missed. In this paper, we formulate a proactive contact tracing problem to identify the superspreaders using maximum-likelihood estimation, graph traversal and deep learning algorithms. This problem is challenging due to its sheer combinatorial complexity, problem scale and the fact that the underlying infection network topology is rarely known. We propose a deep learning-based framework using Graph Neural Networks to iteratively refine the supervised learning of proactive contact tracing networks using smaller infection networks and to identify the superspreader. By optimizing the graph traversal and topological features for deep learning, proactive contact tracing strategies can be developed to contain superspreading in an epidemic outbreak.

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

Development of Personnel Epidemic Prevention Monitoring Access Control System

Because of coronavirus variants, it is necessary to pay attention to epidemic prevention measures in the cultivation or product packaging processes. In addition to giving customers more peace of mind when using the products, it also ensures that operators wear masks, work clothes and gloves in the work area. This paper constructs an access control system for personnel epidemic prevention monitoring, which uses IoTtalk [1] to connect IoT devices (such as magnetic reed switches, intelligent switches, RFID readers, and RFID wristbands), utilizes RFID for personnel identification, and employs real-time streaming protocol [2] to take the image of IP Cam for YOLOv4 [3] identification program. The identification program detects whether the personnel is indeed wearing the required equipment. If the personnel is not wearing the required device, the detector will trigger a push broadcast system constructed by LINE Notify to inform the operator for processing. Moreover, we developed an emergency entry mechanism;if an emergency happens, the personnel can trigger the emergency door opening by swiping the card multiple times within a specified time. This function allows the person to enter without wearing the required equipment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

[Epidemiological investigation on the local epidemic situation in Zhengzhou High-Tech Zone caused by SARS-CoV-2 Delta variant].

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.

Osmoprocessor for enabling highly sensitive biomarker detection via lateral flow assays

Lateral flow assays are low-cost devices suitable for point-of-care testing, particularly in low-resource settings. However, some of the lateral flow assays exhibit limited diagnostic utility because the assays can only sample <100uL specimen and the biomarker concentration is significantly lower than the assay detection limit, which compromise the sensitivity. To address the challenge, we have developed the osmoprocessor that statically and spontaneously concentrated biomarkers via osmosis. The specimen in the device interfaces with the aqueous polymer solution via a dialysis membrane. The polymer solution induces an osmotic pressure difference that extracts water from the specimen, while the membrane retains the biomarkers. The evaluation demonstrated that osmosis induced by various water-soluble polymers efficiently extracted water, ca. 15 mL/hr. The water transport kinetics can be adjusted by varying polymer molecular weights and mass concentrations. The osmoprocessor concentrated the specimens to improve the lateral flow assays' detection limits for the model analytes-human chorionic gonadotropin and SARS-CoV-2 nucleocapsid protein. The device processed a 10 mL specimen into a 100uL concentrated sample. Then, the lateral flow assays detected the corresponding biomarkers in the concentrated specimens. The test band intensities of the assays with the concentrated specimens were very similar to the reference assays with 100-fold concentrations. The mass spectrometry analysis estimated the SARSCoV- nucleocapsid protein concentration increased ca. 200-fold after the osmosis. With its simplicity and flexibility, this device demonstrates a great potential to be utilized in conjunction with the existing lateral flow assays for enabling highly sensitive detection of dilute target analytes.

[Epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, China].

Objective: To understand the epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, such as incubation period, serial interval, basic reproductive number (R0) and the influence of gathering places on R0, and provide evidence for the prevention and control of Omicron variant infection. Methods: The data of daily confirmed cases of Omicron variant infection from April 8 to May 8, 2022 in two COVID-19 outbreaks in Guangzhou were collected for model fitting. Weibull, Gamma and lognormal distribution were used to estimate incubation period and serial interval. Exponential growth method and the maximum likelihood estimation were used to estimate R0. Results: The median of incubation period was 2.94 (95%CI: 2.52-3.38) days and median of serial interval was 3.32 (95%CI: 2.89-3.81) days. The estimated R0 in small-size place was 4.40 (95%CI: 3.95-4.85), while the estimated R0 at airport was 11.35 (95%CI: 11.02-11.67). Conclusion: The incubation period of Omicron variant in two local COVID-19 outbreaks in Guangzhou is significantly shorter than that of delta variant. The higher the gathering degree in a place, the larger the R0. Due to its rapid transmission, COVID-19 epidemic is prone to occur. Therefore, the COVID-19 prevention and control strategy should be dynamically adjusted in time.

Investigation on molecular characters of sars-cov-2 nsp8 gene and protein

Objective  To explore SARS-CoV-2 nsp8 genetic variation, Nsp8 protein structure, biological function and targeted drugs, and to lay foundation for establishing more effective prevention and control strategies.  Methods  Analyses of nsp8 genetic variability, physical and chemical characteristics, spatial structure, antigenic epitopes, biological function, and drug combined targets of Nsp8 were carried out using bioinformatics technology and large biological databases.  Results  Based on nsp8 sequences of 28 isolates of coronavirus of three species, evolutionary tree was successfully constructed. SARS-CoV-2 isolates showed 99%-100% conservation of nsp8 genes, less genetic distance to SARS than MERS isolates. Nsp8 had no signal peptide and transmembrane area. In reticulocytes in vitro, Nsp8 had a half-life of 4 h and was hydrophilic. A secondary model and a tertiary structure model were established. Linear B cell and CTL antigenic epitopes, phosphorylation and SUMB modification sites were found in Nsp8. Using the DrugBank database, four drugs targeted Nsp8 were obtained.  Conclusions  Nsp8 possesses the characteristics of typical antigens, participates in viral replication, and various isolates of the same species share high conservation of nsp8 gene, suggesting potential applications in researches on pathogenic mechanism, genotyping and prevention of this virus. Notably, this is the first report on Nsp8-targeted chemotherapeutic drugs, and the findings can be of considerable scientific significance and application value, under the conditions that measures with special effect for COVID-19 prevention and control are urgently needed. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context

The worldwide coronavirus disease 2019 (COVID-19) pandemic has greatly affected the power system operations as a result of the great changes of socio-economic behaviours. This paper proposes a short-term load forecasting method in COVID-19 context based on temporal-spatial model. In the spatial scale, the cross-domain couplings analysis of multi-factor in COVID-19 dataset is performed by means of copula theory, while COVID-19 time-series data is decomposed via variational mode decomposition algorithm into different intrinsic mode functions in the temporal scale. The forecasting values of load demand can then be acquired by combining forecasted IMFs from light Gradient Boosting Machine (LightGBM) algorithm. The performance and superiority of the proposed temporal-spatial forecasting model are evaluated and verified through a comprehensive cross-domain dataset.

Chasing Play on TikTok from Populations with Disabilities to Inspire Playful and Inclusive Technology Design

There is an open call for technology to be more playful [5, 79] and for tech design to be more inclusive of people with disabilities [80]. In the era of COVID19, it is often unsafe for the public in general and people with disabilities, in particular, to engage in in-person design exercises using traditional methods. This presents a missed opportunity as these populations are already sharing playful content rich with tacit design knowledge that can be used to inspire the design of playful everyday technology. We present our process of scraping play potentials [4] from TikTok from content creators with disabilities to generate design concepts that may inspire future technology design. We share 7 emerging themes from the scraped content, a catalog of design concepts that may inspire designers, and discuss the relevance of the emerging themes and possible implications for the design concepts.

Hyperglycosylated spike of SARS-CoV-2 gamma variant induces breast cancer metastasis

SARS-CoV-2 exploits the host cellular machinery for virus replication leading to the acute syndrome of coronavirus disease 2019 (COVID-19). Growing evidence suggests SARS-CoV-2 also exacerbates many chronic diseases, including cancers. As mutations on the spike protein (S) emerged as dominant variants that reduce vaccine efficacy, little is known about the relation between SARS-CoV-2 virus variants and cancers. Compared to the SARS-CoV-2 wild-type, the Gamma variant contains two additional NXT/S glycosylation motifs on the S protein. The hyperglycosylated S of Gamma variant is more stable, resulting in more significant epithelial-mesenchymal transition (EMT) potential. SARS-CoV-2 infection promoted NF-κB signaling activation and p65 nuclear translocation, inducing Snail expression. Pharmacologic inhibition of NF-κB activity by nature food compound, I3C suppressed viral replication and Gamma variant-mediated breast cancer metastasis, indicating that NF-κB inhibition can reduce chronic disease in COVID-19 patients. Our study revealed that the Gamma variant of SARS-CoV-2 activates NF-κB and, in turn, triggers the pro-survival function for cancer progression.

Investigating covid-19 news before and after the soft lock-down: An example from Taiwan

COVID-19 caused an unprecedented public health crisis and was declared a global pandemic on 11 March 2020, by the World Health Organization. The Taiwanese government’s early deployment mitigated the effect of the pandemic, yet the breakout in May 2021 brought a new chal-lenge. This study focuses on examining Taiwanese newspaper articles regarding the government response before and after the soft lockdown, collecting 125,570 articles reported by three major news channels from 31 December 2019, to 30 June 2021, and splitting them into four stages. Latent Dirichlet Allocation topic modeling and sentiment analysis were used to depict the overall picture of Taiwan’s pandemic. While the news media focused on the impact and shock of the pandemic in the initial stage, prevention measures were more present in the last stage. Then, to focus on the government response indicators, we retrieved 31,089 related news from 125,570 news articles and catego-rized them into ten indicators, finding the news centered on the fundamental measures that were taken early and that were transformed into advanced measures in the latest and hardest period of the pandemic. Furthermore, this paper examines the temporal distribution of the news related to each indicator with the support of a sentiment analysis of the news’ titles and content, indicating the preparation of Taiwanese society to confront the pandemic. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

An Improved SEIR Model for Reconstructing the Dynamic Transmission of COVID-19

With the recent outbreak of coronavirus disease 2019 (COVID-19), human life and the world economy have been severely affected, the propagation and scale of COVID-19 is top of mind for everyone. To reconstruct the development trend of COVID-19, we investigate the issue of the epidemic spreading process under vigorous non-pharmaceutical interventions. Here, an improved Susceptible-Exposed-Infectious-Recovered (SEIR) model with dynamic variables (i.e., health exposure individuals and close contacts) is proposed to predict the scale of COVID-19 and its dynamic evolution. We assume that the number of contacts and the reproduction number of COVID-19 changes dynamically over time. Then a gradient descent method is applied to estimate the effective reproduction number. We use the proposed model to reconstruct the dynamic transmission of COVID-19 in Chongqing between 14 January and 24 March 2020. The results show a similar development trend with a real-world epidemic. Our work has important implications when considering strategies for continuing surveillance and interventions to eventually contain outbreaks of COVID-19.

[Evaluation of demand of resources for laboratory testing and prevention and control of COVID-19 in the context of global pandemic].

Objective: To rapidly evaluate the level of healthcare resource demand for laboratory testing and prevention and control of corona virus disease 2019 (COVID-19) in different epidemic situation, and prepare for the capacity planning, stockpile distribution, and funding raising for infectious disease epidemic response. Methods: An susceptible, exposed, infectious, removed infectious disease dynamics model with confirmed asymptomatic infection cases and symptomatic hospitalized patients was introduced to simulate different COVID-19 epidemic situation and predict the numbers of hospitalized or isolated patients, and based on the current COVID-19 prevention and control measures in China, the demands of resources for laboratory testing and prevention and control of COVID-19 were evaluated. Results: When community or local transmission or outbreaks occur and total population nucleic acid testing is implemented, the need for human resources is 3.3-89.1 times higher than the reserved, and the current resources of medical personal protective equipment and instruments can meet the need. The surge in asymptomatic infections can also increase the human resource demand for laboratory testing and pose challenge to the prevention and control of the disease. When vaccine protection coverage reach ≥50%, appropriate adjustment of the prevention and control measures can reduce the need for laboratory and human resources. Conclusions: There is a great need in our country to reserve the human resources for laboratory testing and disease prevention and control for the response of the possible epidemic of COVID-19. Challenges to human resources resulted from total population nucleic acid testing and its necessity need to be considered. Conducting non-pharmaceutical interventions and encouraging more people to be vaccinated can mitigate the shock on healthcare resource demand in COVID-19 prevention and control.

Using focus group interviews to investigate the willingness of medical personnel to voluntarily participate in COVID-19 group tasks in a one government quarantine facility in north Taiwan

Objectives: The Taiwan Centers for Disease Control have established government quarantine facilities (GQF) to prevent the community spread of COVID-19. Medical personnel are frontline workers who must have contact with individuals in quarantine and are thus at higher risk of infection. As a reference for future recruitment, this study investigated why some medical personnel volunteer to care for individuals in GQF. Methods: The researchers used purposive sampling to recruit participants from a GQF in northern Taiwan. Focus group interviews were administered. The consistency of the interview outline content was 91%. After the interviews, content analysis was performed to examine and summarize the data. The data were coded before comparison, categorization and summarization, producing various core categories. Results: A total of 12 participants voluntarily attended our interviews, compromising 4 men and 8 women, with an average age of 48.1 ±6.4 years and an average length of service in GQF of 82.1 ± 77.1 days. Our study results revealed four aspects and six reasons that affect the voluntary participation of medical personnel in epidemic prevention tasks. The first aspect was meeting safety needs, and the reasons included sufficient personal protective equipment, complete standard operating procedures, and orientation training and in-service education and training. The second aspect was meeting the needs of love and belonging, and the reason included a good working atmosphere. The third aspect was meeting the need for self-esteem, and the reason included Taiwan's previous SARS epidemic prevention experience. Finally, the fourth aspect was meeting the need for selfrealization, and the reason was based on the humanistic concern. Conclusions: At the time of the coronavirus outbreak, various countries reported insufficient medical personnel or personnel unwilling to participate in epidemic prevention work. Our results revealed that if the environment met the Maslow's hierarchy of needs, it can attract medical personnel to volunteer in GQF tasks. In formulating policies, the needs must be met at all levels, so that medical staff would be willing to participate in epidemic prevention work. © 2021 Chinese Public Health Association of Taiwan. All rights reserved.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

[Preliminary analysis on COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province].

Objective: To analyze the characteristics of COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province. Methods: The daily incidence data and case information of COVID-19 were collected from the official websites of provincial and municipal health commissions. The morbidity rate, severity rate, case-fatality rate, and spread ratio of COVID-19 were calculated. Results: As of 20 March, 2020, a total of 12 941 cases of COVID-19 had been conformed, including 116 deaths, and the average morbidity rate, severity rate and case-fatality rate were 0.97/100 000, 13.5% and 0.90%, respectively. The morbidity rates in Zhejiang (2.12/100 000), Jiangxi (2.01/100 000) and Beijing (1.93/100 000) ranked top three. The characteristics of COVID-19 case spectrum varied from province to province. The first three provinces (autonomous region, municipality) with high severity rates were Tianjin (45.6%), Xinjiang (35.5%) and Heilongjiang (29.5%). The case-fatality rate was highest in Xinjiang (3.95%), followed by Hainan (3.57%) and Heilongjiang (2.70%). The average spread ratio was 0.98 and the spread intensity varied from province to province. Tibet had the lowest spread ratio (0), followed by Qinghai (0.20) and Guangdong (0.23). Conclusion: The intervention measures were effective in preventing the spread of COVID-19 and improved treatment effect in China. However, there were significant differences among different regions in severity, case-fatality rate and spread ratio.

[The preliminary analysis on the characteristics of the cluster for the COVID-19].

Since December 2019, COVID-19, a new emerging infection disease, has spread in 27 countries and regions. The clusters of many cases were reported with the epidemic progresses. We collected currently available information for 377 COVID-19 clusters (1 719 cases), excluded the hospital clusters and Hubei cases, during the period from January 1 to February 20, 2020. There were 297 family clusters (79%), case median was 4; 39 clusters of dining (10%), case median was 5; 23 clusters of shopping malls or supermarkets (6%), case median was 13; 12 clusters of work units (3%), case median was 6, and 6 clusters of transportation. We selected 325 cases to estimate the incubation period and its range was 1 to 20 days, median was 7 days, and mode was 4 days. The analysis of the epidemic situation in a department store in China indicated that there was a possibility of patients as the source of infection during the incubation period of the epidemic. From February 5 to 21, 2020, 634 persons were infected on the Diamond Princess Liner. All persons are susceptible to the 2019 coronavirus. Age, patients during the incubation period and the worse environment might be the cause of the cases rising. The progress of the two typical outbreaks clearly demonstrated the spread of the early cases in Wuhan. In conclusion, screening and isolating close contacts remained essential other than clinical treatment during the epidemic. Especially for the healthy people in the epidemic area, isolation was the key.