Longitudinal wastewater surveillance addressed public health priorities during the transition from "dynamic COVID-zero" to "opening up" in China: a population-based study (preprint)

Background Wastewater surveillance provides real-time, cost-effective monitoring of SARS-CoV-2 transmission. We developed the first city-level wastewater warning system in mainland China, located in Shenzhen. Our study aimed to reveal cryptic transmissions under the "dynamic COVID-zero" policy and characterize the dynamics of the infected population and variant prevalence, and then guide the allocation of medical resources during the transition to "opening up" in China. Methods In this population-based study, a total of 1,204 COVID-19 cases were enrolled to evaluate the contribution of Omicron variant-specific faecal shedding rates in wastewater. After that, wastewater samples from up to 334 sites distributed in communities and port areas in two districts of Shenzhen covering 1.74 million people were tested daily to evaluate the sensitivity and specificity of this approach and were validated against daily SARS-CoV-2 screening. After the public health policy was switched to "opening up" in December 7, 2022, we conducted wastewater surveillance at wastewater treatment plants and pump stations covering 3.55 million people to estimate infected populations using model prediction and detect the relative abundance of SARS-CoV-2 lineages using wastewater sequencing. Findings In total, 82.4% of SARS-CoV-2 Omicron cases tested positive for faecal viral RNA within the first four days after the diagnosis, which was far more than the proportion of the ancestral variant. A total of 27,759 wastewater samples were detected from July 26 to November 30 in 2022, showing a sensitivity of 73.8% and a specificity of 99.8%. We further found that wastewater surveillance played roles in providing early warnings and revealing cryptic transmissions in two communities. Based on the above results, we employed a prediction model to monitor the daily number of infected individuals in Shenzhen during the transition to "opening up" in China, with over 80% of the population infected in both Futian District and Nanshan District. Notably, the prediction of the daily number of hospital admission was consistent with the actual number. Further sequencing revealed that the Omicron subvariant BA.5.2.48 accounted for the most abundant SARS-CoV-2 RNA in wastewater, and BF.7.14 and BA.5.2.49 ranked second and third, respectively, which was consistent with the clinical sequencing. Interpretation This study provides a scalable solution for wastewater surveillance of SARS-CoV-2 to provide real-time monitoring of the new variants, infected populations and facilitate the precise prediction of hospital admission. This novel framework could be a One Health system for the surveillance of other infectious and emerging pathogens with faecal shedding and antibiotic resistance genes in the future. Funding Sanming Project of Medicine in Shenzhen, Shenzhen Key Medical Discipline Construction Fund.

Density Map-based vehicle counting in remote sensing images with limited resolution

Observing traffic flow is of great significance to contemporary urban management. Overhead images, as represented by remote sensing images, provide a major source of information about traffic flow. However, the spatial resolutions of most common high-resolution remote sensing images are often limited to 0.5 m and even below, which makes it unrealistic to count vehicles by means of widely used object detection methods. Therefore, to explore the potential of remote sensing data for studying global urban development and management, this paper introduces a density map-based vehicle counting method for remote sensing imagery with limited resolution. Density map-based models regard the vehicle counting task as estimating the density of vehicle targets in terms of pixel values. We propose an improved CNN-based network, called Congested Scene Recognition Network Minus (CSRNet—), that generates a density map of vehicles from the input remote sensing imagery. A new dataset, RSVC2021, which was generated from the public DOTA and ITCVD datasets, is also introduced for network training and testing. A benchmark on the RSVC2021 dataset is accordingly established and CSRNet— is selected as the baseline model for subsequent experiments. A set of GF-2 time series images with a resolution of 1 m taken before, during and after the COVID-19 epidemic lockdown covering Wuhan city are applied for real-world application testing. The testing results on both the RSVC2021 dataset and real satellite images confirm that, in terms of both the counting values and the visualized density maps, the proposed method achieves good performance and exhibits considerable application potential in this task. The generating codes of RSVC2021 dataset will be publicly available at https://github.com/YinongGuo/RSVC2021-Dataset.

Effect of COVID-19 on epidemiological characteristics of road traffic injuries in Suzhou: a retrospective study.

BACKGROUND: To present the new trends in epidemiology of road traffic injuries (RTIs) during the Coronavirus disease 2019 (COVID-19) pandemic in Suzhou. METHODS: Pre-hospital records of RTIs from January to May in 2020 and the same period in 2019 were obtained from the database of Suzhou pre-hospital emergency center, Jiangsu, China. Data were extracted for analysis, including demographic characteristics, pre-hospital vital signs, transport, shock index, consciousness, pre-hospital death. A retrospective study comparing epidemiological characteristics of RTIs in Suzhou during the 5-month period in 2020 to the parallel period in 2019 was performed. RESULTS: A total of 7288 RTIs in 2020 and 8869 in 2019 met inclusion criteria. The overall volume of RTIs has statistical difference between the 2 years (p < 0.001), with fewer RTIs in 2020 compared with 2019. Electric bicycle related RTIs increased during the pandemic (2641, 36.24% vs 2380, 26.84%, p < 0.001), with a higher incidence of RTIs with disorder of consciousness (DOC) (7.22% vs 6.13%, p = 0.006). CONCLUSIONS: Under the impact of COVID-19, the total number of RTIs in Suzhou from January to May 2020 decreased. This observation was coupled with a rise in electric bicycle related injuries and an increase in the incidence of RTIs with DOC.

Identification of four linear B-cell epitopes on the SARS-CoV-2 spike protein able to elicit neutralizing antibodiesIdentification of four linear B-cell epitopes on the SARS-CoV-2 spike protein able to elicit neutralizing antibodies (preprint)

SARS-CoV-2 unprecedentedly threatens the public health at worldwide level. There is an urgent need to develop an effective vaccine within a highly accelerated time. Here, we present the most comprehensive S-protein-based linear B-cell epitope candidate list by combining epitopes predicted by eight widely-used immune-informatics methods with the epitopes curated from literature published between Feb 6, 2020 and July 10, 2020. We find four top prioritized linear B-cell epitopes in the hotspot regions of S protein can specifically bind with pooled serum antibodies from horses, mice, and monkeys inoculated with different SARS-CoV-2 vaccine candidates or five patients recovering from COVID-19. The four linear B-cell epitopes can induce neutralizing antibodies against both pseudo and live SARS-CoV-2 virus in immunized wild-type BALB/c mice. This study suggests that the four linear B-cell epitopes are potentially important candidates for serological assay or vaccine development.