Investigation of a family cluster outbreak of coronavirus disease 2019 in Xi-an. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To investigate the transmission characteristics of a family cluster outbreak of coronavirus disease 2019 (COVID-19) in Xi-an, in order to provide reference for prevention and control efforts.

Epidemiological characteristics of COVID-19 in Xi'an city. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To analyze the epidemiological characteristics of confirmed cases of coronavirus disease 2019 (COVID-19) in Xi'an, so as to provide scientific basis for the prevention and control measures.

Vaccination and Government Stringent Control as Effective Strategies in Preventing SARS-CoV-2 Infections: A Global Perspective.

With the rapid implementation of global vaccination against the coronavirus disease 2019 (COVID-19), the threat posed by the disease has been mitigated, yet it remains a major global public health concern. Few studies have estimated the effects of vaccination and government stringent control measures on the disease transmission from a global perspective. To address this, we collected 216 countries' data on COVID-19 daily reported cases, daily vaccinations, daily government stringency indexes (GSIs), and the human development index (HDI) from the dataset of the World Health Organization (WHO) and the Our World in Data COVID-19 (OWID). We utilized the interrupted time series (ITS) model to examine how the incidence was affected by the vaccination and GSI at continental and country levels from 22 January 2020 to 13 February 2022. We found that the effectiveness of vaccination was better in Europe, North America, and Africa than in Asia, South America, and Oceania. The long-term effects outperformed the short-term effects in most cases. Countries with a high HDI usually had a high vaccination coverage, resulting in better vaccination effects. Nonetheless, some countries with high vaccination coverage did not receive a relatively low incidence due to the weaker GSI. The results suggest that in addition to increasing population vaccination coverage, it is crucial to maintain a certain level of government stringent measures to prevent and control the disease. The strategy is particularly appropriate for countries with low vaccination coverage at present.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

Alterations in the Composition of Intestinal DNA Virome in Patients With COVID-19.

Patients with Coronavirus Disease 2019 (COVID-19), due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection mainly present with respiratory issues and related symptoms, in addition to significantly affected digestive system, especially the intestinal tract. While several studies have shown changes in the intestinal flora of patients with COVID-19, not much information is available on the gut virome of such patients. In this study, we used the viromescan software on the latest gut virome database to analyze the intestinal DNA virome composition of 15 patients with COVID-19 and investigated the characteristic alternations, particularly of the intestinal DNA virome to further explore the influence of COVID-19 on the human gut. The DNA viruses in the gut of patients with COVID-19 were mainly crAss-like phages (35.48%), Myoviridae (20.91%), and Siphoviridae (20.43%) family of viruses. Compared with healthy controls, the gut virome composition of patients with COVID-19 changed significantly, especially the crAss-like phages family, from the first time of hospital admission. A potential correlation is also indicated between the change in virome and bacteriome (like Tectiviridae and Bacteroidaceae). The abundance of the viral and bacterial population was also analyzed through continuous sample collection from the gut of patients hospitalized due to COVID-19. The gut virome is indeed affected by the SARS-CoV-2 infection, and along with gut bacteriome, it may play an important role in the disease progression of COVID-19. These conclusions would be helpful in understanding the gut-related response and contribute to the treatment and prevention strategies of COVID-19.

From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus?

Influenza shares the same putative transmission pathway with coronavirus disease 2019 (COVID-19), and causes tremendous morbidity and mortality annually globally. Since the transmission of COVID-19 in China, a series of non-pharmaceutical interventions (NPIs) against to the disease have been implemented to contain its transmission. Based on the surveillance data of influenza, Search Engine Index, and meteorological factors from 2011 to 2021 in Xi'an, and the different level of emergence responses for COVID-19 from 2020 to 2021, Bayesian Structural Time Series model and interrupted time series analysis were applied to quantitatively assess the impact of NPIs in sequent phases with different intensities, and to estimate the reduction of influenza infections. From 2011 to 2021, a total of 197,528 confirmed cases of influenza were reported in Xi'an, and the incidence of influenza continuously increased from 2011 to 2019, especially, in 2019–2020, when the incidence was up to 975.90 per 100,000 persons;however, it showed a sharp reduction of 97.68% in 2020–2021, and of 87.22% in 2021, comparing with 2019–2020. The highest impact on reduction of influenza was observed in the phase of strict implementation of NPIs with an inclusion probability of 0.54. The weekly influenza incidence was reduced by 95.45%, and an approximate reduction of 210,100 (95% CI: 125,100–329,500) influenza infections was found during the post-COVID-19 period. The reduction exhibited significant variations in the geographical, population, and temporal distribution. Our findings demonstrated that NPIs against COVID-19 had a long-term impact on the reduction of influenza transmission.

Alterations in the Composition of Intestinal DNA Virome in Patients With COVID-19

Patients with Coronavirus Disease 2019 (COVID-19), due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection mainly present with respiratory issues and related symptoms, in addition to significantly affected digestive system, especially the intestinal tract. While several studies have shown changes in the intestinal flora of patients with COVID-19, not much information is available on the gut virome of such patients. In this study, we used the viromescan software on the latest gut virome database to analyze the intestinal DNA virome composition of 15 patients with COVID-19 and investigated the characteristic alternations, particularly of the intestinal DNA virome to further explore the influence of COVID-19 on the human gut. The DNA viruses in the gut of patients with COVID-19 were mainly crAss-like phages (35.48%), Myoviridae (20.91%), and Siphoviridae (20.43%) family of viruses. Compared with healthy controls, the gut virome composition of patients with COVID-19 changed significantly, especially the crAss-like phages family, from the first time of hospital admission. A potential correlation is also indicated between the change in virome and bacteriome (like Tectiviridae and Bacteroidaceae). The abundance of the viral and bacterial population was also analyzed through continuous sample collection from the gut of patients hospitalized due to COVID-19. The gut virome is indeed affected by the SARS-CoV-2 infection, and along with gut bacteriome, it may play an important role in the disease progression of COVID-19. These conclusions would be helpful in understanding the gut-related response and contribute to the treatment and prevention strategies of COVID-19.

A Cluster Transmission of Coronavirus Disease 2019 and the Prevention and Control Measures in the Early Stage of the Epidemic in Xi'an, China, 2020.

BACKGROUND At the beginning of the COVID-19 pandemic, a cluster outbreak caused by an imported case from Hubei Province was reported in Xi'an City, Shaanxi Province, China. Ten patients from 2 families and 1 hospital were involved in the transmission. MATERIAL AND METHODS We conducted an epidemiological investigation to identify the cluster transmission of COVID-19. The demographic, epidemiological, clinical, laboratory, and cluster characteristics were described and analyzed. RESULTS From January 27 to February 13, 2020, a total of 10 individuals were confirmed to be infected with SARS-CoV-2 by the nucleic acid testing of nasopharyngeal swabs from 2 families and 1 hospital. Among the confirmed cases, 7 had atypical clinical symptoms and 3 were asymptomatic. The median times from onset to diagnosis and to discharge were 3.5 days (range, 1-5 days) and 19.5 days (range, 16-38 days), respectively. There were 4 patients whose exposure dates were 1, 3, 3, and 2 days earlier than the onset dates of their previous-generation cases, respectively. Four prevention and control measures were effectively used to interrupt the disease transmission. CONCLUSIONS SARS-CoV-2 can be easily transmitted within families and in hospitals, and asymptomatic patients could act as a source of disease transmission. The results of this outbreak at the early epidemic stage support the recommendation that individuals with confirmed COVID-19 and all their close contacts should be subjected to medical quarantined observation and nucleic acid screening as early as possible, even if they do not have any symptoms. Meanwhile, people in high-risk areas should improve their protective measures.

Population Movement, City Closure in Wuhan, and Geographical Expansion of the COVID-19 Infection in China in January 2020.

BACKGROUND: The unprecedented outbreak of corona virus disease 2019 (COVID-19) infection in Wuhan City has caused global concern; the outflow of the population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city-closure measure to prevent its transmission considering the large amount of travel before the Chinese New Year. METHODS: Based on the daily reported new cases and the population-movement data between 1 and 31 January, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan using different closing-date scenarios. RESULTS: We observed a significantly positive association between population movement and the number of the COVID-19 cases. The spatial distribution of cases per unit of outflow population indicated that the infection in some areas with a large outflow of population might have been underestimated, such as Henan and Hunan provinces. Further analysis revealed that if the city-closure policy had been implemented 2 days earlier, 1420 (95% confidence interval, 1059-1833) cases could have been prevented, and if 2 days later, 1462 (1090-1886) more cases would have been possible. CONCLUSIONS: Our findings suggest that population movement might be one important trigger for the transmission of COVID-19 infection in China, and the policy of city closure is effective in controlling the epidemic.

SIR model analysis and short-term prediction of epidemic progress of corona virus disease 2019 in China

Objective: To predict the short-term progress of corona virus disease 2019(COVID-19) and evaluate the degree of population control among different provinces.

A systematic meta-analysis of immune signatures in patients with COVID-19.

Currently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been on the rise worldwide. Predicting outcome in COVID-19 remains challenging, and the search for more robust predictors continues. We made a systematic meta-analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non-severe patients of COVID-19, serum levels of Interleukins (IL)-2, IL-2R, IL-4, IL-6, IL-8, IL-10 and tumor necrosis factor α were significantly up-regulated in severe patients, with the largest inter-group differences observed for IL-6 and IL-10. In contrast, IL-5, IL-1ß and Interferon (IFN)-γ did not show significant inter-group difference. Four mediators of T cells count, including CD3+ T, CD4+ T, CD8+ T, CD4+ CD25+ CD127- Treg, together with CD19+ B cells count and CD16+ CD56+ NK cells were all consistently and significantly depressed in severe group than in non-severe group. SARS-CoV-2 specific IgA and IgG antibodies were significantly higher in severe group than in non-severe group, while IgM antibody in the severe patients was slightly lower than those in the non-severe patients, and IgE antibody showed no significant inter-group differences. The combination of cytokines, especially IL-6 and IL-10, and T cell related immune signatures can be used as robust biomarkers to predict disease severity following SARS-CoV-2 infection.

Epidemiological characteristics of the clusters of COVID-19 in Xi'an

Objective: To explore the transmission characteristics of the typical clusters of coronavirus diseases 2019 (COVID-19) in Xi'an, so as to provide scientific basis for optimizing the control strategy of COVID-19.

Early transmission dynamics of COVID-19 in Shaanxi Province

OBJECTIVE: To explore the early features of COVID-19 epidemic in Shaanxi Province so as to provide scientific basis for optimizing the prevention strategies and evaluating the effects of interventions. METHODS: The epidemic data that were reported through official networks of Shaanxi Province from Dec. 31, 2019 to Feb. 13, 2020 and the case data from Chinese Information System for Disease Control and Prevention were collected, the population data during the same period were obtained from Shaanxi Statistical Yearbook. The descriptive epidemiological analysis was performed by using Excel and ArcGIS software, the transmission dynamics model of COVID-19 was built based on Berkeley Madonna software experiment platform, and the rules of occurrence and progression of the disease were observed. RESULTS: By Feb. 13, 2020, the accumulative confirmed cases of COVID-19 reached 230 in Shaanxi Province, and the incidence rate was about 0.59 per 100 000. The male cases were more than the female cases, and the patients aged between 40 and 50 years old were dominant. The COVID-19 was highly prevalent in Xi'an, Ankang and Hanzhong. The SEIAR model showed that the basic regeneration index(R0) of the epidemic in Shaanxi Province was about 2.95, concluding that the beginning of Feb. 2020 was the peak period of outbreak of COVID-19 in Shaanxi Province. CONCLUSION: The COVID-19 epidemic in Shaanxi province shows a fast spreading trend. The theoretical number of confirmed cases that is predicted based on the SEIAR model can provide basis for prevention and control of the COVID-19 epidemic and curb the spread of the epidemic.