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.

Towards an Understanding of the Engagement and Emotional Behaviour of MOOC Students using Sentiment and Semantic Features

Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.

Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach

PM2.5, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online or offline observations and numerical models, which are generally high economic cost- or labor-intensive. In this study, we develop a new method, named Multi-Tracer Estimation Algorithm (MTEA), to identify the primary and secondary components from routine observation of PM2.5. By comparing with long-term and short-term measurements of aerosol chemical components in China and the United States, it is proven that MTEA can successfully capture the magnitude and variation of the primary PM2.5 (PPM) and secondary PM2.5 (SPM). Applying MTEA to the China National Air Quality Network, we find that (1) SPM accounted for 63.5 % of the PM2.5 in cities in southern China on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China, and at the same time the secondary proportion in regional background regions was ∼ 19 % higher than that in populous regions;(2) the summertime secondary PM2.5 proportion presented a slight but consistent increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly because of the recent increase in O3 pollution in China;(3) the secondary PM2.5 proportion in Beijing significantly increased by 34 % during the COVID-19 lockdown, which might be the main reason for the observed unexpected PM pollution in this special period;and finally, (4) SPM and O3 showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but the correlations between total PM2.5 and O3 in these two regions, as determined from PPM levels, were quite different. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for future PM mitigation.

Using portable air purifiers to reduce airborne transmission of infectious respiratory viruses - a computational fluid dynamics study (preprint)

Aerosols and droplets generated from expiratory events play a critical role in the transmission of infectious respiratory viruses. Increasingly robust evidence has suggested the crucial role of fine aerosols in airborne transmission of respiratory diseases, which is now widely regarded as an important transmission path of COVID-19. In this report, we used CFD modelling to investigate the efficiency of using portable air purifiers containing HEPA filters to reduce airborne aerosols in hospitals and serve as a potential retrofit mitigation strategy. We used a consulting room to set up our simulations because currently the clearance time between consultations is the controlling factor that limits the patient turnover rate. The results suggest the inlet/suction of the air purifier unit should be lifted above the floor to achieve better clearance efficiency, with up to 40% improvement possible. If multiple air purifiers are used, the combined efficiency can increase to 62%. This work provides practical guidance on a mitigation strategy that can be easily implemented in an expedient, cost-effective and rapid manner, and paves the way for developing more science-informed strategies to mitigate the airborne transmission of respiratory infections in hospitals.

Bank Systemic Risk around COVID-19: A Cross-Country Analysis (preprint)

Using 1,584 listed banks from 65 countries during the COVID-19 pandemic, we conduct the first broad-based international study examining the effect of the pandemic on bank systemic risk. We find the pandemic increases systemic risk across countries. The effect operates through government policy and bank default risk channels. Additional analysis suggests that the adverse effect of the pandemic on systemic stability is more pronounced for large, highly leveraged, riskier, high loan-to-asset, undercapitalized, and low network centrality banks. However, this effect is moderated by formal bank regulation (e.g., deposit insurance) and ownership structure (e.g., foreign and government ownership), and informal institutions (e.g., culture and trust).

Frequency and accuracy of proactive testing for COVID-19 (preprint)

The SARS-CoV-2 coronavirus has proven difficult to control not only because of its high transmissibility, but because those who are infected readily spread the virus before symptoms appear, and because some infected individuals, though contagious, never exhibit symptoms. Proactive testing of asymptomatic individuals is therefore a powerful, and probably necessary, tool for preventing widespread infection in many settings. This paper explores the effectiveness of alternative testing regimes, in which the frequency, the accuracy, and the delay between testing and results determine the time path of infection. For a simple model of disease transmission, we present analytic formulas that determine the effect of testing on the expected number of days of during which an infectious individual is exposed to the population at large. This allows us to estimate the frequency of testing that would be required to prevent uncontrolled outbreaks, and to explore the trade-offs between frequency, accuracy, and delay in achieving this objective. We conclude by discussing applications to outbreak control on college and university campuses.