[Analysis on the infection source of the first local cluster epidemic caused by the VOC/Gamma variant of SARS-CoV-2 in China].

Objective: To investigate a SARS-CoV-2 epidemic reported in Rongcheng City, Weihai, Shandong Province. Methods: The SARS-CoV-2 nucleic acid positive patients and their close contacts were investigated, and the whole genome sequencing and genetic evolution analysis of 9 variant viruses were carried out. An infection source investigation and analysis were carried out from two sources of home and abroad, and three aspects of human, material and environment. Results: A total of 15 asymptomatic infections were reported in this epidemic, including 13 cases as employees of workshop of aquatic products processing company, with an infection rate of 21.67% (13/60). Two cases were infected people's neighbors in the same village (conjugal relation). The first six positive persons were processing workers engaged in the first process of removing squid viscera in the workshop of the company. The nucleic acid Ct value of the first time were concentrated between 15 and 29, suggesting that the virus load was high, which was suspected to be caused by one-time homologous exposure. The whole genome sequence of 9 SARS-CoV-2 strains was highly homologous, belonging to VOC/Gamma (Lineage P.1.15). No highly homologous sequences were found from previous native and imported cases in China. It was highly homologous with the six virus sequences sampled from May 5 to 26, 2021 uploaded by Chile. The infection source investigation showed that the company had used the squid raw materials captured in the ocean near Chile and Argentina from May to June 2021 over the last 14 days. Many samples of raw materials, products and their outer packages in the inventory were tested positive for nucleic acid. Conclusion: This epidemic is the first local epidemic caused by the VOC/Gamma of SARS-CoV-2 in China. It is speculated that the VOC/Gamma, which was prevalent in South America from May to June 2021, could be imported into China through frozen squid.

Advances and challenges in research on SARS-CoV-2 subunit vaccine candidates

The Coronavirus Disease 2019 (COVID-19) pand emic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought unprecedented pressure on communities, medical systems and economy all over the world. Candidate vaccines have been generated using existing technologies that provides us with the hope to effectively deal with COVID-19. Subunit vaccines are one of the prominent options in the "primer-boost" strategy, and have become the main force in the new candidate vaccines. This paper reviews the latest progress in clinical trials and platforms technology and highlight the challenges encountered in the development of COVID-19 subunit vaccine.

Spatiotemporal Distribution and Ecological Risk Assessment of Plastic Additives in Taihu Lake

To reveal the spatiotemporal distribution and risks of plastic additives in Taihu Lake during the COVID-19 pandemic, the occurrences of typical bisphenols, phthalate esters, and benzotriazoles in the surface water of Taihu Lake were investigated. The plastic additives in 19 sites in Taihu Lake were monitored in four seasons, and their potential ecological risks were evaluated. Diethylphthalate (DEP), dimethoxyethyl phthalate (DMEP), benzyl butyl phthalate (BBP), bisphenol A (BPA), and 2-(2H-benzotriazol-2-yl)-4, 6-di-tert-pentylphenol (UV-328) were detected, with detection rates of 100%, 97%, 58%, 98%, and 7%, respectively. During the COVID-19 pandemic, the sharply increasing usage of plastic products did not result in a significant increase in the plastic additives pollution in Taihu Lake. Conversely, the pollution of plastic additives showed a decreasing trend due to reduced human activities. There were significant seasonal differences in the concentrations of plastic additives in Taihu Lake. The average concentrations of plastic additives in spring and summer were 104.7 and 100.3 ng•L-1, respectively, which were higher than those in autumn (30.7 ng•L-1) and winter (29.9 ng•L-1). The plastic additive pollution also showed some differences in spatial distribution. The concentrations of plastic additives near the southwest coast of Taihu Lake were higher than those in other monitoring sites. The presence of plastic additives in Taihu Lake showed low risks to algae with the proportion of 30%. The risks in autumn and winter were higher than those in spring and summer. BPA and UV-328 may have been the main risk factors, which should be of concern. © 2022, Science Press. All right reserved.

Map Design for Public Health Emergencies: a Novel Conceptual Framework for Thematic Content Selection

Public health emergencies always lead to serious consequences which affect a lot on human health and socioeconomic progress. It is essential that governments and regional health commissions guide the public toward self-protection and better arranged social production during epidemic outbreaks and spreads. According to the need of risk communication and information disclosure, existing studies for COVID-19 maps and visualization applications are conducive to predicting the future trend of the pandemic, mitigating the harmful effect on public wellbeing by leading to effective intervention and policy measures. However, unsettled tasks remain on comprehensive organization of risk information, effective expression of data for public requirement, and systematic theoretical framework as a standard of map design for public health emergencies. To close the research gaps, this paper proposes a conceptual framework with a three-dimensional spatiotemporal-logic structure as a theoretical foundation for map thematic content selection, which is also a good basis for determining the effective visualization approaches of map design. It enhances the validity and legibility of the map expression by leading maps' thematic content couple with features and processes of an epidemic. Then, using the COVID-19 outbreak in Shenzhen, China, as an example, this paper illustrates how to apply the conceptual framework for selecting the thematic content of COVID-19 maps, and explains the specific ways to transform epidemic data into objects for cartographic representation with proper principles and modes. To our knowledge, this paper is the very first study to bring the thematic content of maps for public health emergencies to the fore, and it is thus believed to shed fresh lights into thematic map design.

Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3 18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://github.com/HKBU-HPML/CovidNet3D.

Supervised framework for COVID-19 classification and lesion localization from chest CT

Background: Quick and precise identification of people suspected of having COVID-19 plays a key function in imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but also helps in the development of an improved health system. Building a deep-learning framework for automated identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic. Aim: To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and localization of swellings. Methods: In all cases, subjects' chest areas were segmented by means of a pre-trained U-Net;the segmented 3D chest areas were submitted as inputs to a 3D deep neural network to forecast the likelihood of infection with COVID-19;the swellings were restricted by joining the initiation areas within the classification system and the unsupervised linked elements. A total of 499 3D CT scans were utilized for training worldwide and 131 3D CT scans were utilized for verification. Results: The algorithm took only 1.93 seconds to process the CT amount of a single affected person using a special graphics processing unit (GPU). Interesting results were obtained in terms of the development of societal challenges and better health policy. Conclusions: The deep-learning model can precisely forecast COVID-19 infectious probabilities and detect swelling areas in chest CT, with no requirement for training swellings. The easy-to-train and high-functioning deep-learning algorithm offers a fast method to classify people affected by COVID-19, which is useful to monitor the SARS-CoV-2 epidemic.

[Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform].

Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.

[Application of healthcare big data in active case finding of COVID-19 in Yinzhou district of Ningbo].

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.