Estimating the daily trend in the size of the COVID-19 infected population in Wuhan.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10 940 confirmed cases outside Hubei province. METHODS: In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan. RESULTS: We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [CI]: 3139-3321) and 51 273 (95% CI: 49 844-52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5-1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0-40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4-73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8-100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019. CONCLUSIONS: In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

CRISPR/Cas12a-Enabled Multiplex Biosensing Strategy Via an Affordable and Visual Nylon Membrane Readout.

Most multiplex nucleic acids detection methods require numerous reagents and high-priced instruments. The emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas has been regarded as a promising point-of-care (POC) strategy for nucleic acids detection. However, how to achieve CRISPR/Cas multiplex biosensing remains a challenge. Here, an affordable means termed CRISPR-RDB (CRISPR-based reverse dot blot) for multiplex target detection in parallel, which possesses the advantages of high sensitivity and specificity, cost-effectiveness, instrument-free, ease to use, and visualization is reported. CRISPR-RDB integrates the trans-cleavage activity of CRISPR-Cas12a with a commercial RDB technique. It utilizes different Cas12a-crRNA complexes to separately identify multiple targets in one sample and converts targeted information into colorimetric signals on a piece of accessible nylon membrane that attaches corresponding specific-oligonucleotide probes. It has demonstrated that the versatility of CRISPR-RDB by constructing a four-channel system to simultaneously detect influenza A, influenza B, respiratory syncytial virus, and SARS-CoV-2. With a simple modification of crRNAs, the CRISPR-RDB can be modified to detect human papillomavirus, saving two-thirds of the time compared to a commercial PCR-RDB kit. Further, a user-friendly microchip system for convenient use, as well as a smartphone app for signal interpretation, is engineered. CRISPR-RDB represents a desirable option for multiplexed biosensing and on-site diagnosis.

Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation.

The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.

Optimism and friendship quality as mediators between trait emotional intelligence and life satisfaction in Chinese adolescents: A two-wave longitudinal study.

Using a convenience sample of adolescents (N = 1609; 63.5% female; M age = 16.54), this study explored whether EI predicted adolescent life satisfaction and whether friendship quality and optimism mediated this relationship during the COVID-19 pandemic. The structural equation modeling revealed that EI predicted adolescent life satisfaction, friendship quality, and optimism, friendship quality partially mediated the relationship between EI and life satisfaction, and optimism partially mediated the relationship between EI and friendship quality. These findings prove that psychological or educative approaches focused on EI could increase life satisfaction in adolescents during difficult times such as COVID-19, but EI may be linked with life satisfaction via friendship quality only. Training in optimism approaches and friendship quality enhancement programs could also effectively promote life satisfaction.

17α-Hydroxyprogesterone Caproate Inhibits Cytokine Production via Suppression of NF-κB Activation.

Cytokine release syndrome (CRS) is one of the leading causes of morbidity and mortality in COVID-19 patients with elevated levels of circulating cytokines contributing to various clinical symptoms. Favorable control of CRS represents a promising and effective strategy to mitigate the clinical outcomes of hospitalized patients with moderate to severe pneumonia. Using in vivo cytokine release assay in human peripheral blood mononuclear cell (PBMC)-engrafted immunodeficient mice, we reported that 17α-hydroxyprogesterone caproate (17-OHPC), a synthetic progestogen, exhibited significant inhibition of OKT-3-stimulated production of numerous cytokines including TNF-α, IFN-γ, IL-2, IL-4, IL-6, IL-10, and GM-CSF. Furthermore, 17-OHPC inhibited in vitro production of IFN-γ, IL-1ß, IL-2, IL-6, and IL-10 in human PBMCs stimulated with OKT3, while exhibiting down-regulation of the mRNA levels of TNF-α, IFN-γ, IL-2, IL-6, and IL-10. Using the same human PBMCs, additional stimulators anti-CD28 antibody or PHA treatments led to substantial cytokine production, which was also attenuated by 17-OHPC. OKT3-stimulated phosphorylation of IκBα and nuclear translocation of NF-κB p65 in human PBMCs were also reversed by 17-OHPC, suggesting its inhibition on NF-κB signaling in immune cells. Taken together, this work reported both in vivo and in vitro inhibition of cytokine production by 17-OHPC, presumably by virtue of its suppression of NF-κB signaling. These findings provide pharmacological evidence to support the potential application of 17-OHPC in treating CRS associated with COVID-19.

A SARS-CoV-2 vaccine based on conjugation of SARS-CoV-2 RBD with IC28 peptide and mannan.

SARS-CoV-2 is a particularly transmissible virus that causes a severe respiratory disease known as COVID-19. Safe and effective vaccines are urgently needed to combat the COVID-19 pandemic. The receptor-binding domain (RBD) of SARS-CoV-2 spike protein elicits most neutralizing antibodies during viral infection and is an ideal antigen for vaccine development. In particular, RBD expressed by E. coli is amenable to low cost and high-yield manufacturability. The adjuvant is necessitated to improve the immunogenicity of RBD. IC28, a TLR5-dependent adjuvant, is a peptide from bacterial flagellin. Mannan is a ligand of TLR-4 or TLR-2 and a polysaccharide adjuvant. Here, IC28 and mannan were both covalently conjugated with RBD from E. coli. The conjugate (RBD-IC28-M) elicited high RBD-specific IgG titers, and a neutralization antibody titer of 201.4. It induced high levels of Th1-type cytokines (IFN-γ) and Th2-type cytokines (IL-5 and IL-10), along with high antigenicity and no apparent toxicity to the organs. The mouse sera of the RBD-IC28-M group competitively interfered with the interaction of RBD and ACE2. Thus, conjugation with IC28 and mannan additively enhanced the humoral and cellular immunity. Our study was expected to provide the feasibility to develop an affordable, easily scalable, effective vaccine SARS-CoV-2 vaccine.

Social media mining under the COVID-19 context: Progress, challenges, and opportunities.

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Conjugation with 8-arm PEG and CRM197 enhances the immunogenicity of SARS-CoV-2 ORF8 protein.

Safe and effective vaccines are urgently needed to combat the COVID-19 pandemic. However, the SARS-CoV-2 variants raise concerns about the effectiveness of vaccines. As a SARS-CoV-2 antigen target, ORF8 strongly inhibits the IFN-ß and NF-κB-responsive promoter, and can be potentially used for the development of SARS-CoV-2 vaccine. However, it is necessary to improve the immunogenicity of ORF8 by adjuvants or delivery systems. CRM197 was a carrier protein with the ability to activate T helper cells for antigens. Eight-arm PEG could conjugate multiple antigen molecules in one entity with inherent adjuvant effect. In the present study, ORF8 was conjugated with CRM197 and 8-arm PEG, respectively. The cellular and humoral immune responses to the conjugates (ORF8-CRM and ORF8-PEG) were evaluated in the BALB/c mice. As compared with ORF8-CRM and ORF8 administrated with aluminum adjuvant (ORF8/AL), ORF8-PEG induced a higher ORF8-specific IgG titer (2.6 × 104), higher levels of cytokines (IFN-γ, TNF-α, IFN-ß, and IL-5), stronger splenocyte proliferation. Thus, conjugation with 8-arm PEG was an effective method to improve the immune response to ORF8. Moreover, ORF8-PEG did not lead to apparent toxicity to the cardiac, liver and renal functions. ORF8-PEG was expected to act as an effective vaccine to provide the immune protection against SARS-CoV-2.

Urban-regional disparities in mental health signals in Australia during the COVID-19 pandemic: a study via Twitter data and machine learning models

This study establishes a novel empirical framework using machine learning techniques to measure the urban-regional disparity of the public's mental health signals in Australia during the pandemic, and to examine the interrelationships amongst mental health, demographic and socioeconomic profiles of neighbourhoods, health risks and healthcare access. Our results show that the public's mental health signals in capital cities were better than those in regional areas. The negative mental health signals in capital cities are associated with a lower level of income, more crowded living space, a lower level of healthcare availability and more difficulties in healthcare access.

The association of intensity and duration of non-pharmacological interventions and implementation of vaccination with COVID-19 infection, death, and excess mortality: Natural experiment in 22 European countries.

BACKGROUND: Critical questions remain regarding the need for intensity to continue NPIs as the public was vaccinated. We evaluated the association of intensity and duration of non-pharmaceutical interventions (NPIs) and vaccines with COVID-19 infection, death, and excess mortality in Europe. METHODS: Data comes from Our Word in Data. We included 22 European countries from January 20, 2020, to May 30, 2021. The time-varying constrained distribution lag model was used in each country to estimate the impact of different intensities and duration of NPIs on COVID-19 control, considering vaccination coverage. Country-specific effects were pooled through meta-analysis. RESULTS: This study found that high-intensity and long-duration of NPIs showed a positive main effect on reducing infection in the absence of vaccines, especially in the intensity above the 80th percentile and lasted for 7 days (RR = 0.93, 95% CI: 0.89-0.98). However, the adverse effect on excess mortality also increased with the duration and intensity. Specifically, it was associated with an increase of 44.16% (RR = 1.44, 95% CI: 1.27-1.64) in the excess mortality under the strict intervention (the intensity above the 80th percentile and lasted for 21 days). As the vaccine rollouts, the inhibition of the strict intervention on cases growth rate was increased (RR dropped from 0.95 to 0.87). Simultaneously, vaccination also alleviated the negative impact of the strict intervention on excess mortality (RR decreased from 1.44 to 1.25). Besides, maintaining the strict intervention appeared to more reduce the cases, as well as avoids more overall burden of death compared with weak intervention. CONCLUSIONS: Our study highlights the importance of continued high-intensity NPIs in low vaccine coverage. Lifting of NPIs in insufficient vaccination coverage may cause increased infections and death burden. Policymakers should coordinate the intensity and duration of NPIs and allocate medical resources reasonably with widespread vaccination.

Measurement of contagion spatial spread probability in public places: A case study on COVID-19.

The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Identification of a recombinant equine coronavirus in donkey, China.

Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain - NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation.

Human mobility and COVID-19 transmission: a systematic review and future directions

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration;2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic;3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease;and 4) to enrich the source of mobility data to ensure data accuracy and suability. [ FROM AUTHOR] Copyright of Annals of GIS is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia.

INTRODUCTION: Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. METHODS: Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public's mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. RESULTS: Australians' mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. CONCLUSION: We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds.

[Recent Developments and Future Directions of Oral Healthcare System and Dental Public Health System in China in Light of the Current Global Emergency].

The study is aimed to help promote the development of the oral healthcare system and dental public health system in China and to help achieve the goal of improving the nation's oral health. We herein provided an overview and critical evaluation of recent developments in oral healthcare systems and dental public health systems in China and other countries, and discussed a number of potential directions for the future development of dental public health. The current global public health emergency of the coronavirus disease 2019 (COVID-19) pandemic was also taken into account in our discussions. Thus, to facilitate the accomplishment of the goals of the Healthy China 2030 Program, we suggested the establishment of a community-based, prevention-oriented model for the oral healthcare system and dental public health system. The model we proposed features the integration of oral and general health services, the utilization of technological innovations and big data concerning health, and a forceful promotion of remote dental services focused on prevention and early diagnosis and treatment. Furthermore, under the background of COVID-19 becoming a normal part of people's lives, we should adopt differentiated prevention and protection measures and emergency response preplans appropriate for the actual epidemic situation of a particular region so that clinical services are strengthened while unnecessary wastes of resources are avoided. We should actively explore for alternative approaches to care in the face of special circumstances.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data.

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.