Incidence, risk factors, and clinical symptom profile of reinfection during Omicron-dominated COVID-19 outbreak in Hong Kong: A retrospective cohort study (preprint)

Background: Despite the declaration from World Health Organization of the end of the COVID-19 pandemic, reinfection persists and continues to strain the global healthcare system. With the emergence of the most recent variant of SARS-CoV-2 named JN.1, retrospective analysis of epidemiological characteristics of previous cases involving the Omicron variant is essential to provide references for preventing reinfection caused by the ongoing new SARS-Cov-2 variants. Methods: This retrospective cohort study included 6325 patients infected with SARS-CoV-2 during the Omicron-dominated outbreak (from December 2021 to May 2022) in Hong Kong. Statistical analysis was conducted to demonstrate the epidemiological characteristics and a logistic regression model was utilized to identify risk factors associated with reinfection. Results: The Omicron reinfection incidence was 5.18% (n = 353). No significant difference was observed in receiving mRNA (BNT162b2) vaccine and inactivated (CoronaVac) vaccine between reinfection and non-reinfection groups (p>0.05). Risk factors were identified as female gender (p<0.001), longer infection duration (p<0.05), comorbidity of eyes, ear, nose, throat disease (p<0.01), and severe post-infection impact on daily life and work (p<0.05), while equal or larger than 70 years old (p<0.05) and vaccination after primary infection (p<0.01) were associated with a lower risk of reinfection. The prevalence of most symptoms after reinfection was lower than the first infection, except for fatigue. Conclusion: No significant difference in mRNA (BNT162b2) vaccine and inactivated (CoronaVac) vaccine against reinfection. Post-infection vaccination could lower the risk of reinfection, which potentially inform the development of preventive measures including vaccination policies against potential new SARS-Cov-2 variants.

Predicting Delayed Shock in Multisystem Inflammatory Disease in Children: A Multicenter Analysis From the New York City Tri-State Region.

OBJECTIVES: Patients with multisystem inflammatory disease in children (MIS-C) are at risk of developing shock. Our objectives were to determine independent predictors associated with development of delayed shock (≥3 hours from emergency department [ED] arrival) in patients with MIS-C and to derive a model predicting those at low risk for delayed shock. METHODS: We conducted a retrospective cross-sectional study of 22 pediatric EDs in the New York City tri-state area. We included patients meeting World Health Organization criteria for MIS-C and presented April 1 to June 30, 2020. Our main outcomes were to determine the association between clinical and laboratory factors to the development of delayed shock and to derive a laboratory-based prediction model based on identified independent predictors. RESULTS: Of 248 children with MIS-C, 87 (35%) had shock and 58 (66%) had delayed shock. A C-reactive protein (CRP) level greater than 20 mg/dL (adjusted odds ratio [aOR], 5.3; 95% confidence interval [CI], 2.4-12.1), lymphocyte percent less than 11% (aOR, 3.8; 95% CI, 1.7-8.6), and platelet count less than 220,000/uL (aOR, 4.2; 95% CI, 1.8-9.8) were independently associated with delayed shock. A prediction model including a CRP level less than 6 mg/dL, lymphocyte percent more than 20%, and platelet count more than 260,000/uL, categorized patients with MIS-C at low risk of developing delayed shock (sensitivity 93% [95% CI, 66-100], specificity 38% [95% CI, 22-55]). CONCLUSIONS: Serum CRP, lymphocyte percent, and platelet count differentiated children at higher and lower risk for developing delayed shock. Use of these data can stratify the risk of progression to shock in patients with MIS-C, providing situational awareness and helping guide their level of care.

Bioinformatics analysis based on high-throughput sequencing data to identify hub genes related to different clinical types of COVID-19.

This article aims to explore hub genes related to different clinical types of cases with COVID-19 and predict the therapeutic drugs related to severe cases. The expression profile of GSE166424 was divided into four data sets according to different clinical types of COVID-19 and then calculated the differential expression genes (DEGs). The specific genes of four clinical types of COVID-19 were obtained by Venn diagram and conducted enrichment analysis, protein-protein interaction (PPI) networks analysis, screening hub genes, and ROC curve analysis. The hub genes related to severe cases were verified in GSE171110, their RNA-specific expression tissues were obtained from the HPA database, and potential therapeutic drugs were predicted through the DGIdb database. There were 536, 266, 944, and 506 specific genes related to asymptomatic infections, mild, moderate, and severe cases, respectively. The hub genes of severe specific genes were AURKB, BRCA1, BUB1, CCNB1, CCNB2, CDC20, CDC6, KIF11, TOP2A, UBE2C, and RPL11, and also differentially expressed in GSE171110 (P < 0.05), and their AUC values were greater than 0.955. The RNA tissue specificity of AURKB, CDC6, KIF11, UBE2C, CCNB2, CDC20, TOP2A, BUB1, and CCNB1 specifically enhanced on lymphoid tissue; CCNB2, CDC20, TOP2A, and BUB1 specifically expressed on the testis. Finally, 55 drugs related to severe COVID-19 were obtained from the DGIdb database. Summary, AURKB, BRCA1, BUB1, CCNB1, CCNB2, CDC20, CDC6, KIF11, TOP2A, UBE2C, and RPL11 may be potential diagnostic biomarkers for severe COVID-19, which may affect immune and male reproductive systems. 55 drugs may be potential therapeutic drugs for severe COVID-19.

Multisystem Inflammatory Syndrome in Children.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a newly recognized condition affecting children with recent infection or exposure to coronavirus disease 2019 (COVID-19). MIS-C has symptoms that affect multiple organs systems, with some clinical features resembling Kawasaki disease (KD) and toxic shock syndrome (TSS). OBJECTIVE OF THE REVIEW: Our goal was to review the current literature and describe the evaluation and treatment algorithms for children suspected of having MIS-C who present to the emergency department. DISCUSSION: MIS-C has a wide clinical spectrum and diagnosis is based on a combination of both clinical and laboratory findings. The exact mechanism of immune dysregulation of MIS-C is not well understood. Physical findings may evolve and do not necessarily appear at the same time. Gastrointestinal, cardiac, inflammatory, and coagulopathy manifestations and dysfunction are seen frequently in MIS-C. CONCLUSIONS: The diagnosis of MIS-C is based on clinical presentation and specific laboratory findings. In the emergency setting, a high level of suspicion for MIS-C is required in patients exposed to COVID-19. Early diagnosis and prompt initiation of therapy offer the best chance for optimal outcomes.

How agricultural economists are using big data: a review

Purpose>The purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.Design/methodology/approach>This study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.Findings>This study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.Originality/value>This study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.

Using Hashtags to Analyze Purpose and Technology Application of Open-Source Project Related to COVID-19 (preprint)

COVID-19 has had a profound impact on the lives of all human beings. Emerging technologies have made significant contributions to the fight against the pandemic. An extensive review of the application of technology will help facilitate future research and technology development to provide better solutions for future pandemics. In contrast to the extensive surveys of academic communities that have already been conducted, this study explores the IT community of practice. Using GitHub as the study target, we analyzed the main functionalities of the projects submitted during the pandemic. This study examines trends in projects with different functionalities and the relationship between functionalities and technologies. The study results show an imbalance in the number of projects with varying functionalities in the GitHub community, i.e., applications account for more than half of the projects. In contrast, other data analysis and AI projects account for a smaller share. This differs significantly from the survey of the academic community, where the findings focus more on cutting-edge technologies while projects in the community of practice use more mature technologies. The spontaneous behavior of developers may lack organization and make it challenging to target needs.

Pediatric emergency care in New York City during the COVID-19 pandemic shutdown and reopening periods.

OBJECTIVE: New York City (NYC) is home to the largest public healthcare system in the United States and was an early epicenter of coronavirus disease 2019 (COVID-19) infections. This system serves as the safety net for underserved and marginalized communities disproportionately affected by the pandemic. Prior studies reported substantial declines in pediatric emergency department (ED) volume during the initial pandemic surge, but few describe the ongoing impact of COVID-19 throughout the year. We evaluated the characteristics of pediatric ED visits to NYC public hospitals during the pandemic lockdown and reopening periods of 2020 compared to the prior year. METHODS: Retrospective cross-sectional analysis of pediatric ED visits from 11 NYC public hospitals from January 2019-December 2020. Visit demographics, throughput times, and diagnosis information during the early (3/7/20-6/7/20) and late (6/8/20-12/31/20) pandemic periods coinciding with the New York State of emergency declaration (3/7/20) and the first reopening date (6/7/20) were compared to similar time periods in 2019. Findings were correlated with key pandemic shutdown and reopening events. RESULTS: There was a 47% decrease in ED volume in 2020 compared to 2019 (125,649 versus 238,024 visits). After reopening orders began in June 2020, volumes increased but peaked at <60% of 2019 volumes. Admission rates, triage acuity, and risk of presenting with a serious medical illness were significantly higher in 2020 versus 2019 (P < 0.001). Time-to-provider times decreased however provider-to-disposition times increased during the pandemic (P < 0.001). Infectious and asthma diagnoses declined >70% during the pandemic in contrast to the year prior. After reopening periods began, penetrating traumatic injuries significantly increased compared to 2019 [+34%, Relative Risk: 3.2 (2.6, 3.8)]. CONCLUSIONS: NYC public hospitals experienced a sharp decrease in pediatric volume but an increase in patient acuity during both the initial pandemic surge and through the reopening periods. As COVID-19 variants emerge, the threat of the current pandemic expanding remains. Understanding its influence on pediatric ED utilization can optimize resource allocation and ensure equitable care for future surge events.

Evidence Mapping of 23 Systematic Reviews of Traditional Chinese Medicine Combined With Western Medicine Approaches for COVID-19

Background: Coronavirus disease 2019 (COVID-19) has already spread around the world. The modality of traditional Chinese medicine (TCM) combined with Western medicine (WM) approaches is being used to treat COVID-19 patients in China. Several systematic reviews (SRs) are available highlighting the efficacy and safety of TCM combined with WM approaches in COVID-19 patients. However, their evidence quality is not completely validated. Purpose: We aimed to assess the methodological quality and the risk of bias of the included SRs, assess the evidence quality of outcomes, and present their trends and gaps using the evidence mapping method. Methods: PubMed, Cochrane Library, Embase, CNKI, CBM, and Wanfang Data were searched from inception until March 2021 to identify SRs pertaining to the field of TCM combined with WM approaches for COVID-19. The methodological quality of the SRs was assessed using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2), the risk of bias of the included SRs was assessed with the Risk of Bias in Systematic Review (ROBIS) tool, and the evidence quality of outcomes was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Results: In total, 23 SRs were found eligible. Twenty-one were rated of moderate confidence by AMSTAR 2, while 12 were rated at low risk using the ROBIS tool. In addition, most outcomes were graded as having moderate quality using the GRADE system. We found that the combined use of TCM and WM approaches could improve the CT recovery rate, effective rate, viral nucleic acid negative conversion rate, and the disappearance rate of fever, cough, and shortness of breath. Also, these approaches could decrease the conversion rate from mild to critical, white blood cell counts, and lymphocyte counts and shorten the time to viral assay conversion and the length of hospital stay. Conclusion: TCM combined with WM approaches had advantages in efficacy, laboratory, and clinical symptom outcomes of COVID-19, but the methodological deficiencies of SRs should be taken into consideration. Therefore, to better guide clinical practice in the future, the methodological quality of SRs should still be improved, and high-quality randomized controlled trials (RCTs) and observational studies should also be carried out.

Mechanism of optimal time-course COVID-19 vaccine prioritization based on non-Markovian steady-state prediction (preprint)

Vaccination is essential for controlling the coronavirus disease (COVID-19) pandemic. An effective time-course strategy for the allocation of COVID-19 vaccines is crucial given that the global vaccine supply will still be limited in some countries/regions in the near future and that mutant strains have emerged and will continue to spread worldwide. Both asymptomatic and symptomatic transmission have played major roles in the COVID-19 pandemic, which can only be properly described as a typical non-Markovian process. However, the prioritization of vaccines in the non-Markovian framework still lacks sufficient research, and the underlying mechanism of the time-course vaccine allocation optimization has not yet been uncovered. In this paper, based on an age-stratified compartmental model calibrated through clinical and epidemiological data, we propose optimal vaccination strategies (OVS) through steady-state prediction in the non-Markovian framework. This OVS outperforms other empirical vaccine prioritization approaches in minimizing cumulative infections, cumulative deaths, or years of life lost caused by the pandemic. We found that there exists a fast decline in the prevention efficiency of vaccination if vaccines are solely administered to a selected age group, which indicates that the widely adopted strategy to continuously vaccinate high-risk group is not optimal. Through mathematical analysis of the model, we reveal that dynamic vaccine allocations to combinations of different age groups is necessary to achieve optimal vaccine prioritization. Our work not only provides meaningful references for vaccination in countries currently lacking vaccines and for vaccine allocation strategies to prevent mutant strains in the future, but also reveals the mechanism of dynamic vaccine allocation optimization, forming a theoretical and modelling framework empirically applicable to the optimal time-course prioritization.

Mechanism of Optimal Time-Course COVID-19 Vaccine Prioritization Based on Non-Markovian Steady-State Prediction (preprint)

Vaccination is essential for controlling the coronavirus disease (COVID-19) pandemic. An effective time-course strategy for the allocation of COVID-19 vaccines is crucial given that the global vaccine supply will still be limited in some countries/regions in the near future and that mutant strains have emerged and will continue to spread worldwide. Both asymptomatic and symptomatic transmission have played major roles in the COVID-19 pandemic, which can only be properly described as a typical non-Markovian process. However, the prioritization of vaccines in the non-Markovian framework still lacks sufficient research, and the underlying mechanism of the time-course vaccine allocation optimization has not yet been uncovered. In this paper, based on an age-stratified compartmental model calibrated through clinical and epidemiological data, we propose optimal vaccination strategies (OVS) through steady-state prediction in the non-Markovian framework. This OVS outperforms other empirical vaccine prioritization approaches in minimizing cumulative infections, cumulative deaths, or years of life lost caused by the pandemic. We found that there exists a fast decline in the prevention efficiency of vaccination if vaccines are solely administered to a selected age group, which indicates that the widely adopted strategy to continuously vaccinate high-risk group is not optimal. Through mathematical analysis of the model, we reveal that dynamic vaccine allocations to combinations of different age groups is necessary to achieve optimal vaccine prioritization. Our work not only provides meaningful references for vaccination in countries currently lacking vaccines and for vaccine allocation strategies to prevent mutant strains in the future, but also reveals the mechanism of dynamic vaccine allocation optimization, forming a theoretical and modelling framework empirically applicable to the optimal time-course prioritization.Funding: This work was supported by the Hong Kong Baptist University (HKBU) Strategic Development Fund. This research was conducted using the resources of the High- Performance Computing Cluster Centre at HKBU, which receives funding from the Hong Kong Research Grant Council and the HKBU. Declaration of Interests: The authors declare that they have no competing interests.

Calibrated Intervention and Containment of the COVID-19 Pandemic (preprint)

Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R_0 multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.